NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results

This paper reviews the first NTIRE challenge on quality enhancement of compressed video, with focus on proposed solutions and results. In this challenge, the new Large-scale Diverse Video (LDV) dataset is employed. The challenge has three tracks. Tracks 1 and 2 aim at enhancing the videos compressed by HEVC at a fixed QP, while Track 3 is designed for enhancing the videos compressed by x265 at a fixed bit-rate. Besides, the quality enhancement of Tracks 1 and 3 targets at improving the fidelity (PSNR), and Track 2 targets at enhancing the perceptual quality. The three tracks totally attract 482 registrations. In the test phase, 12 teams, 8 teams and 11 teams submitted the final results of Tracks 1, 2 and 3, respectively. The proposed methods and solutions gauge the state-of-the-art of video quality enhancement. The homepage of the challenge: https://github.com/RenYang-home/NTIRE21_VEnh


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1 Introduction

Ren Yang (ren.yang@vision.ee.ethz.ch) and Radu Timofte (radu.timofte@vision.ee.ethz.ch) are the organizers of the NTIRE 2021 challenge, and other authors participated in the challenge.
The Appendix lists the authors’ teams and affiliations.
NTIRE 2021 website: https://data.vision.ee.ethz.ch/cvl/ntire21/

During the recent years, there is increasing popularity of video streaming over the Internet [Cisco] and the demands on high-quality and high-resolution videos are also rapidly increasing. Due to the limited bandwidth of Internet, video compression [wiegand2003overview, sullivan2012overview] plays an important role to significantly reduce the bit-rate and facilitate transmitting a large number of high-quality and high-resolution videos. However, video compression unavoidably leads to compression artifacts, thus resulting in the loss of both fidelity and perceptual quality and the degradation of Quality of Experience (QoE). Therefore, it is necessary to study on enhancing the quality of compressed video, which aims at improving the compression quality at the decoder side. Due to the rate-distortion trade-off in data compression, enhancing compressed video is equivalent to reducing the bit-rate at the same quality, and hence it also can be seen as a way to improve the efficiency of video compression.

In the past a few years, there has been plenty of works in this direction [yang2017decoder, yang2018enhancing, wang2017novel, lu2018deep, yang2018multi, xu2020multi, yang2019quality, guan2019mfqe, Xu_2019_ICCV, deng2020spatio, yang2020learning, huo2021recurrent, wang2020multi], among which [yang2017decoder, yang2018enhancing, wang2017novel] are single-frame quality enhancement methods, while [lu2018deep, yang2018multi, yang2019quality, guan2019mfqe, Xu_2019_ICCV, deng2020spatio, yang2020learning, huo2021recurrent, wang2020multi] propose enhancing quality by taking advantage of temporal correlation. Besides, [wang2020multi] aims at improving the perceptual quality of compressed video. Other works [yang2017decoder, yang2018enhancing, wang2017novel, lu2018deep, yang2018multi, yang2019quality, guan2019mfqe, Xu_2019_ICCV, deng2020spatio, yang2020learning, huo2021recurrent]

focus on advancing the performance on Peak Signal-to-Noise Ratio (PSNR) to achieve higher fidelity to the uncompressed video. These works show the promising future of this research field. However, the scale of training sets used in the existing methods are incremental and different methods are also tested on various test sets. For example, at the beginning,

[wang2017novel] utilizes the image database BSDS500 [arbelaez2010contour] for training, without any video. Then, [yang2017decoder] trains the model on a small video dataset including 26 video sequences, and [yang2018enhancing] enlarges the training set to 81 videos. In the multi-frame methods, [lu2018deep] is trained on the Vimeo-90K dataset [xue2019video], in which each clip only contains 7 frames, and thus it is insufficient for studying on enhancing long video sequences. Then, the Vid-70 dataset, which includes 70 video sequences, is used as the training set in [yang2018multi, yang2019quality, Xu_2019_ICCV, huo2021recurrent]. Meanwhile, [guan2019mfqe] and [deng2020spatio] collected 142 and 106 uncompressed videos for training, respectively. Besides, the commonly used test sets in existing literature are the JCT-VC dataset [bossen2013common] (18 videos), the test set of Vid-70 [yang2019quality] (10 videos) and Vimeo-90K (only 7 frames in each clip). Standardizing a larger and more diverse dataset is not only beneficial for training video enhancement models but also meaningful for establishing a convincing benchmark in this area.

The NTIRE 2021 challenge on enhancing compressed video is a step forward in benchmarking the video quality enhancement algorithms. It uses the newly proposed Large-scale Diverse Video (LDV) [yang2021ntire_dataset] dataset, which contains 240 videos with the diversities of content, motion and frame-rate, . The LDV dataset is introduced in [yang2021ntire_dataset] along with the analyses of challenge results. In the following, we first describe the NTIRE 2021 challenge, and then introduce the proposed methods and their results.

2 NTIRE 2021 Challenge

The objectives of the NTIRE 2021 challenge on enhancing compressed video are: (i) to gauge and push the state-of-the-art in video quality enhancement; (ii) to compare different solutions; (iii) to promote the newly proposed LDV dataset; and (iv) to promote more challenging video quality enhancement settings.

This challenge is one of the NTIRE 2021 associated challenges: nonhomogeneous dehazing [ancuti2021ntire], defocus deblurring using dual-pixel [abuolaim2021ntire], depth guided image relighting [elhelou2021ntire], image deblurring [nah2021ntire], multi-modal aerial view imagery classification [liu2021ntire]

, learning the super-resolution space 

[lugmayr2021ntire], quality enhancement of compressed video (this report), video super-resolution [son2021ntire], perceptual image quality assessment [gu2021ntire], burst super-resolution [bhat2021ntire], high dynamic range [perez2021ntire].

2.1 LDV dataset

As introduced in [yang2021ntire_dataset], our LDV dataset contains 240 videos with 10 categories of scenes, , animal, city, close-up, fashion, human, indoor, park, scenery, sports and vehicle. Besides, among the 240 videos in LDV, there are 48 fast-motion videos, 68 high frame-rate () videos and 172 low frame-rate () videos. Additionally, the camera is slightly shaky (, captured by handheld camera) in 75 videos of LDV, and 20 videos in LDV are with the dark environments, , at night or in the rooms with insufficient light. In the challenge of NTIRE 2021, we divide the LDV dataset into training, validation and test sets with 200, 20 and 20 videos, respectively. The test set is further split into two sets with 10 videos in each for the tracks of fixed QP (Tracks 1 and 2) and fixed bit-rate (Track 3), respectively. The 20 validation videos consist of the videos from the 10 categories of scenes with two videos in each category. Each test set has one video from each category. Besides, 9 out of the 20 validation videos and 4 among the 10 videos in each test set are with high frame-rates. There are five fast-motion videos in the validation set. In the test sets for fixed QP and fixed bit-rate tracks, there are three and two fast-motion videos, respectively.

#1 #2 #3 #4 #5 #6 #7 #8 #9 #10 Average Average
BILIBILI AI & FDU 33.69 31.80 38.31 34.44 28.00 32.13 29.68 29.91 35.61 31.62 32.52 0.9562
NTU-SLab 31.30 32.46 36.96 35.29 28.30 33.00 30.42 29.20 35.70 32.24 32.49 0.9552
VUE 31.10 32.00 36.36 34.86 28.08 32.26 30.06 28.54 35.31 31.82 32.04 0.9493
NOAHTCV 30.97 31.76 36.25 34.52 28.01 32.11 29.75 28.56 35.38 31.67 31.90 0.9480
Gogoing 30.91 31.68 36.16 34.53 27.99 32.16 29.77 28.45 35.31 31.66 31.86 0.9472
NJU-Vision 30.84 31.55 36.08 34.47 27.92 32.01 29.72 28.42 35.21 31.58 31.78 0.9470
MT.MaxClear 31.15 31.21 37.06 33.83 27.68 31.68 29.52 28.43 34.87 32.03 31.75 0.9473
VIP&DJI 30.75 31.36 36.07 34.35 27.79 31.89 29.48 28.35 35.05 31.47 31.65 0.9452
Shannon 30.81 31.41 35.83 34.17 27.81 31.71 29.53 28.43 35.05 31.49 31.62 0.9457
HNU_CVers 30.74 31.35 35.90 34.21 27.79 31.76 29.49 28.24 34.99 31.47 31.59 0.9443
BOE-IOT-AIBD 30.69 30.95 35.65 33.83 27.51 31.38 29.29 28.21 34.94 31.29 31.37 0.9431
Ivp-tencent 30.53 30.63 35.16 33.73 27.26 31.00 29.22 28.14 34.51 31.14 31.13 0.9405
MFQE [yang2018multi] 30.56 30.67 34.99 33.59 27.38 31.02 29.21 28.03 34.63 31.17 31.12 0.9392
QECNN [yang2018enhancing] 30.46 30.47 34.80 33.48 27.17 30.78 29.15 28.03 34.39 31.05 30.98 0.9381
DnCNN [zhang2017beyond] 30.41 30.40 34.71 33.35 27.12 30.67 29.13 28.00 34.37 31.02 30.92 0.9373
ARCNN [dong2015compression] 30.29 30.18 34.35 33.12 26.91 30.42 29.05 27.97 34.23 30.87 30.74 0.9345
Unprocessed video 30.04 29.95 34.16 32.89 26.79 30.07 28.90 27.84 34.09 30.71 30.54 0.9305
Table 1: The results of Track 1 (fixed QP, fidelity)

2.2 Fidelity tracks

The first part of this challenge aims at improving the quality of compressed video towards fidelity. We evaluate the fidelity via PSNR. Additionally, we also calculate the Multi-Scale Structural SIMilarity index (MS-SSIM) [wang2003multiscale] for the proposed methods.

Track 1: Fixed QP. In Track 1, the videos are compressed following the typical settings of the existing literature [yang2017decoder, yang2018enhancing, wang2017novel, lu2018deep, yang2018multi, xu2020multi, yang2019quality, guan2019mfqe, Xu_2019_ICCV, deng2020spatio, huo2021recurrent, wang2020multi], , using the official HEVC test model (HM) at fixed QPs. In this challenge, we compress videos by the default configuration of the Low-Delay P (LDP) mode (encoder_lowdelay_P_main.cfg) of HM 16.20111https://hevc.hhi.fraunhofer.de/svn/svn_HEVCSoftware/tags/HM-16.20 at QP = 37. In this setting, due to the regularly changed QPs at the frame-level, the compression quality normally fluctuates regularly among frames. Besides, it does not enable the rate control strategy, and therefore the frame-rate does not have impact on compression. This may make this track to be an easy task.

Track 3: Fixed bit-rate. Track 3 targets at a more practical scenario. In video streaming, rate control has been utilizing as a popular strategy to constraint the bit-rate into the limited bandwidth. In this track, we compress videos by the x265 library of FFmpeg222https://johnvansickle.com/ffmpeg/releases/ffmpeg-release-amd64-static.tar.xz with rate control enabled and set the target bit-rate as 200 kbps, by the following commands:

ffmpeg -pix_fmt yuv420p -s WxH -r FR -i name.yuv -c:v libx265 -b:v 200k -x265-params pass=1:log-level=error -f null /dev/null

ffmpeg -pix_fmt yuv420p -s WxH -r FR -i name.yuv -c:v libx265 -b:v 200k -x265-params pass=2:log-level=error name.mkv

Note that we utilize the two-pass scheme to ensure the accuracy of rate control. Due to the fixed bit-rate, the videos of different frame-rates, various motion speeds and diverse contents have to be compressed to a specific bit-rate per second. This makes the compression quality of different videos dramatically different, and therefore may make it a more challenging track than Track 1.

2.3 Perceptual track

We also organize a track aiming at enhancing the compressed videos towards perceptual quality. In this track, the performance is evaluated via the Mean Opinion Score (MOS). We also report the performance on other perceptual metrics as references, such as the Learned Perceptual Image Patch Similarity (LPIPS) [zhang2018unreasonable], Fréchet Inception Distance (FID) [heusel2017gans], Kernel Inception Distance (KID) [binkowski2018demystifying] and Video Multimethod Assessment Fusion (VMAF) [VMAF].

Track 2: perceptual quality enhancement. In Track 2, we compress videos with the same settings as Track 1. The task of this track is to generate visually pleasing enhanced videos, and the scores are ranked according to MOS values from 15 subjects. The scores range from (poorest quality) to (best quality). The groundtruth videos are given to the subjects as the standard of , but the subjects are asked to rate videos in accordance with the visual quality, instead of the similarity to the groundtruth. We linearly normalize the scores () of each subject to


in which and denote the highest and the lowest score of each subject, respectively. In the experiment, we insert five repeated videos to check the concentration of each subject to ensure the consistency of rating. Eventually, we omit the scores from the three least concentrated subjects, and the final MOS values are averaged among 12 subjects. Besides, we also calculate the LPIPS, FID, VID and VMAF values to evaluate the proposed methods.

#1 #2 #3 #4 #5 #6 #7 #8 #9 #10 Average Average Average Average Average
BILIBILI AI & FDU 85 75 67 71 83 61 53 89 64 58 71 0.0429 32.17 0.0137 75.69
NTU-SLab 63 73 61 71 74 62 62 75 62 82 69 0.0483 34.64 0.0179 71.55
NOAHTCV 73 72 73 64 66 84 61 57 61 58 67 0.0561 46.39 0.0288 68.92
Shannon 67 66 74 67 64 65 56 61 57 54 63 0.0561 50.61 0.0332 69.06
VUE 62 67 72 62 73 56 61 36 47 60 60 0.1018 72.27 0.0561 78.64
BOE-IOT-AIBD 50 40 63 66 67 41 58 50 42 50 53 0.0674 62.05 0.0447 68.78
(anonymous) 45 38 70 58 60 33 62 22 69 48 50 0.0865 83.77 0.0699 69.70
MT.MaxClear 34 47 80 62 50 41 59 13 38 34 46 0.1314 92.42 0.0818 77.30
Unprocessed video 34 29 29 53 39 34 35 44 30 34 36 0.0752 48.94 0.0303 65.72
Table 2: The results of Track 2 (fixed QP, perceptual)
#11 #12 #13 #14 #15 #16 #17 #18 #19 #20 Average Average
NTU-SLab 30.59 28.14 35.37 34.61 32.23 34.66 28.17 20.38 27.39 32.13 30.37 0.9484
BILIBILI AI & FDU 29.85 27.01 34.17 34.25 31.62 34.34 28.51 21.13 28.01 30.65 29.95 0.9468
MT.MaxClear 29.47 27.89 35.63 34.16 30.93 34.29 26.25 20.47 27.38 30.38 29.69 0.9423
Block2Rock Noah-Hisilicon 30.20 27.31 34.50 33.55 31.94 34.14 26.62 20.43 26.74 30.96 29.64 0.9405
VUE 29.93 27.31 34.58 33.64 31.79 33.86 26.54 20.44 26.54 30.97 29.56 0.9403
Gogoing 29.77 27.23 34.36 33.47 31.61 33.71 26.68 20.40 26.38 30.77 29.44 0.9393
NOAHTCV 29.80 27.13 34.15 33.38 31.60 33.66 26.38 20.36 26.37 30.64 29.35 0.9379
BLUEDOT 29.74 27.09 34.08 33.29 31.53 33.33 26.50 20.36 26.35 30.57 29.28 0.9384
VIP&DJI 29.64 27.09 34.12 33.44 31.46 33.50 26.50 20.34 26.19 30.56 29.28 0.9380
McEhance 29.57 26.81 33.92 33.10 31.36 33.40 25.94 20.21 26.07 30.27 29.07 0.9353
BOE-IOT-AIBD 29.43 26.68 33.72 33.02 31.04 32.98 26.25 20.26 25.81 30.09 28.93 0.9350
Unprocessed video 29.17 26.02 32.52 32.22 30.69 32.54 25.48 20.03 25.28 29.41 28.34 0.9243
Table 3: The results of Track 3 (fixed bit-rate, fidelity)

3 Challenge results

3.1 Track 1: Fixed QP, Fidelity

The numerical results of Track 1 are shown in Table 1. On the top part, we show the results of the 12 methods proposed in this challenge. The unprocessed video indicates the compressed videos without enhancement. Additionally, we also train the models of the existing methods on the training set of the newly proposed LDV dataset, and report the results in Table 1. It can be seen from Table 1, the proposed methods in the challenge outperform the existing methods, and therefore advance the state-of-the-art of video quality enhancement.

The PSNR improvements of the 12 proposed methods range from 0.59 dB to 1.98 dB, and the improvements of MS-SSIM range between 0.0100 and 0.0257. The BILIBILI AI & FDU Team achieves the best average PSNR and MS-SSIM performance in this track. They improve the average PSNR and MS-SSIM by 1.98 dB and 0.0257, respectively. The NTU-SLab and VUE Teams rank second and third, respectively. The average PSNR performance of NTU-SLab is slightly lower (0.03 dB) than the BILIBILI AI & FDU Team, and the PSNR of VUE is 0.48 dB lower than the best method. We also report the detailed results on the 10 test videos (#1 to #10) in Table 1. The results indicate that the second-ranked team NTU-SLab outperforms BILIBILI AI & FDU on 7 videos. It shows the better generalization capability of NTU-SLab than BILIBILI AI & FDU.

3.2 Track 2: Fixed QP, Perceptual

Table 2 shows the results of Track 2. In Track 2, the BILIBILI AI & FDU Team achieves the best MOS performance on 5 of the 10 test videos and has the best average MOS performance. The results of the NTU-SLab team are the best on 3 videos, and their average MOS performance ranks second. The NOAHTCV Team is the third in the ranking of average MOS. We also report the results of LIPIS, FID, VID and VMAF, which are the popular metrics for evaluating perceptual quality of image and video. It can be seen from Table 2 that BILIBILI AI & FDU, NTU-SLab and NOAHTCV still rank at the first, second and third places on LPIPS, FID and VID. It indicates that these perceptual metrics are effective on measuring the subjective quality. However, the rank on VMAF is obviously different from MOS. Besides, some teams perform worse than the unprocessed videos on LPIPS, FID and VID, while their MOS values are all higher than the unprocessed videos. This may show that the perceptual metrics are not always reliable for evaluating the visual quality of video.

3.3 Track 3: Fixed bit-rate, Fidelity

Table 3 shows the results of Track 3. In this track, we use the different videos as the test set, denoted as #11 to #20. The top three teams in this track are NTU-SLab, BILIBILI AI $ FDU and MT.MaxClear. The NTU-SLab Team achieves the best results on 6 videos and also ranks first on average PSNR and MS-SSIM. They improve the average PSNR by 2.03 dB. BILIBILI AI $ FDU and MT.MaxClear enhance PSNR by 1.61 dB and 1.35 dB, respectively.

Team Running time (s) per frame Platform GPU Ensemble / Fusion Extra training data
Track 1 Track 2 Track 3
BILIBILI AI & FDU 9.00 9.45 9.00 PyTorch Tesla V100/RTX 3090 Flip/Rotation x8 Bilibili [bili], YouTube [youtube]
NTU-SLab 1.36 1.36 1.36 PyTorch Tesla V100 Flip/Rotation x8 Pre-trained on REDS [nah2019ntire]
VUE 34 36 50 PyTorch Tesla V100 Flip/Rotation x8 Vimeo90K [xue2019video]
NOAHTCV 12.8 12.8 12.8 TensorFlow Tesla V100 Flip/Rotation x8 DIV8K [gu2019div8k] (Track 2)
MT.MaxClear 2.4 2.4 2.4 PyTorch Tesla V100 Flip/Rotation/Multi-model x12 Private dataset
Shannon 12.0 1.5 - PyTorch Tesla T4 Flip/Rotation x8 (Track 1) -
Block2Rock Noah-Hisilicon - - 300 PyTorch Tesla V100 Flip/Rotation x8 YouTube [youtube]
Gogoing 8.5 - 6.8 PyTorch Tesla V100 Flip/Rotation x4 REDS [nah2019ntire]
NJUVsion 3.0 - - PyTorch Titan RTX Flip/Rotation x6 SJ4K [song2013sjtu]
BOE-IOT-AIBD 1.16 1.16 1.16 PyTorch GTX 1080 Overlapping patches -
(anonymous) - 4.52 - PyTorch Tesla V100 - Partly finetuned from [wang2019edvr]
VIP&DJI 18.4 - 12.8 PyTorch GTX 1080/2080 Ti Flip/Rotation x8 SkyPixel [SKYPIXEL].
BLUEDOT - - 2.85 PyTorch RTX 3090 - Dataset of MFQE 2.0 [guan2019mfqe]
HNU_CVers 13.72 - - PyTorch RTX 3090 Overlapping patches -
McEhance - - 0.16 PyTorch GTX 1080 Ti - -
Ivp-tencent 0.0078 - - PyTorch GTX 2080 Ti - -
MFQE [yang2018multi] 0.38 - - TensorFlow TITAN Xp - -
QECNN [yang2018enhancing] 0.20 - - TensorFlow TITAN Xp - -
DnCNN [zhang2017beyond] 0.08 - - TensorFlow TITAN Xp - -
ARCNN [dong2015compression] 0.02 - - TensorFlow TITAN Xp - -
Table 4: The reported time complexity, platforms, test strategies and training data of the challenge methods.

3.4 Efficiency

Table 4 reports the running time of the proposed methods. In the methods with top quality performance, NTU-SLab is most the most efficient method. The NTU-SLab and BILIBILI AI & FDU Teams achieve the best and the second best quality performance for all three tracks, but the method of NTU-SLab is several times faster than BILIBILI AI & FDU. Therefore, the NTU-SLab Team makes the best trade-off between quality performance and time efficiency. Moreover, among all proposed methods, the method from the Ivp-tencent Team is most time-efficient. It is able to enhance more than 120 frames per second, so it may be practical for the scenario of high frame-rates.

3.5 Training and test

It can be seen from Table 4 that the top teams utilized extra training data in additional to the 200 training videos pf LDV [yang2021ntire_dataset] provided in the challenge. It may indicate that the scale of training database has obvious effect on the test performance. Besides, the ensemble strategy [timofte2016seven] has been widely used in the top methods, and the participants observe the quality improvement of their methods when using ensemble, showing the effectiveness of the ensemble strategy for video enhancement.

(a) Network architecture.
(b) The proposed pSMGF.
Figure 1: Network architectures of the BILIBILI AI & FDU Team.

4 Challenge methods and teams


Track 1. In Track 1, they propose a Spatiotemporal Model with Gated Fusion (SMGF) [bilibili] for enhancing compressed video, based on the framework of [deng2020spatio]. The pipeline of the proposed method is illustrated in the top of Figure 1-(a).

As the preliminary step, they first extract the metadata from the HEVC bit-stream with HM-Decoder. They set the frame whose QP score in metadata is lower than that of the two adjacent frames (PQF [yang2018multi]) as candidate frame. 1) They fixedly select adjacent frame / of as the first reference frame. 2) Taking the preceding part as example, they recursively take the next preceding candidate frame of the last selected reference frame as a new reference frame until there are reference frames or no candidate frames are left. 3) If there is no more candidate frame and the number of selected reference frames is smaller than

, then repeatedly pad it with the last selected frame until there are


(a) An Overview of BasicVSR++
(b) Flow-guided deformable alignment.
Figure 2: The method proposed by the NTU-SLab Team.

They feed 9 frames (8 references and a target frame) into the Spaito-Temporal Deformable Fusion (STDF) [deng2020spatio] module to capture spatiotemporal information. The output of STDF module is then sent to the Quality Enchancement (QE) module. They employ a stack of adaptive WDSR-A-Block from C2CNet [fuoli2020ntire] as the QE module. As illustrated in Figure 1, a Channel Attention (CA) layer [zhang2018image] is additionally attached at the bottom of WDSR-A-Block [yu2018wide]. Comparing with the CA layer in RCAN [zhang2018image], there are two learnable parameters and initialized with 1 and 0.2 in Ada-WDSR-A-Block. Besides, the channels of the feature map and block in the QE module are 128 and 96, respectively. The channels of Ada-WDSR-A-Block are implemented as {64, 256, 64}.

Additionally, they propose a novel module to improve the performance of enhancement at the bottom of the pipeline. As shown in the middle-top of Figure 1-(a), though each model has the same architecture (STDF with QE) and training strategy (L1 + FFT + Gradient [wang2020scene] loss), one is trained on the official training sets, and the other is on extra videos crawled from Bilibili [bilibili] and YouTube [youtube], named as BiliTube4k. To combine the predictions of two models, they exploit a stack of layers to output the mask and then aggregate predictions. The mask in gated fusion module is with the same resolution of the target frame ranging from , the final enhanced low-quality frame is formulated as


Track 2. In Track 2, they reuse and freeze the models from Track 1, attach ESRGAN [wang2018esrgan] at the bottom of SMGF, and propose a perceptual SMGF (pSMGF). As shown in Figure 1-(b), they first take the enhanced low-quality frames from Track 1. Then they feed these enhanced frames into ESRGAN and train the Generator and Discriminator iteratively. Specifically, they use the ESRGAN pre-trained on DIV2K dataset [agustsson2017ntire], remove the pixel shuffle layer in ESRGAN, and supervise the model with {L1 + FFT + RaGAN + Perceptual} loss. They also utilize the gated fusion module proposed after ESRGAN, which is proposed in SMGF. Specifically, one of the ESRGANs is tuned on the official training sets, and the other is on extra videos collected from Bilibili [bilibili] and YouTube [youtube], named as BiliTube4k. The predictions of two models are aggregated via (2).

Track 3. They utilize the model in Track 1 as the pre-trained model, and then fine-tune it on training data of Track 3 with early stopping. Another difference is that they take the neighboring preceding/following I/P frames as candidate frames, instead of PQFs.

4.2 NTU-SLab Team

Overview. The NTU-SLab Team proposes the BasicVSR++ method for this challenge. BasicVSR++ consists of two deliberate modifications for improving propagation and alignment designs of BasicVSR [chan2021basicvsr]. As shown in Figure 2-(a), given an input video, residual blocks are first applied to extract features from each frame. The features are then propagated under the proposed second-order grid propagation scheme, where alignment is performed by the proposed flow-guided deformable alignment. After propagation, the aggregated features are used to generate the output image through convolution and pixel-shuffling.

Second-Order Grid Propagation. Motivated by the effectiveness of the bidirectional propagation, they devise a grid propagation scheme to enable repeated refinement through propagation

. More specifically, the intermediate features are propagated backward and forward in time in an alternating manner. Through propagation, the information from different frames can be “revisited” and adopted for feature refinement. Compared to existing works that propagate features only once, grid propagation repeatedly extracts information from the entire sequence, improving feature expressiveness. To further enhance the robustness of propagation, they relax the assumption of first-order Markov property in BasicVSR and adopt a second-order connection, realizing a second-order Markov chain. With this relaxation, information can be aggregated from different spatiotemporal locations, improving robustness and effectiveness in occluded and fine regions.

Flow-Guided Deformable Alignment. Deformable alignment [wang2019deformable, wang2019edvr] has demonstrated significant improvements over flow-based alignment [haris2019recurrent, xue2019video] thanks to the offset diversity [chan2021understanding] intrinsically introduced in deformable convolution (DCN) [dai2017deformable, zhu2019deformable]. However, deformable alignment module can be difficult to train [chan2021understanding]. The training instability often results in offset overflow, deteriorating the final performance. To take advantage of the offset diversity while overcoming the instability, they propose to employ optical flow to guide deformable alignment, motivated by the strong relation between deformable alignment and flow-based alignment [chan2021understanding]. The graphical illustration is shown in Figure 2-(b).

Training. The training consists of only one stage. For Tracks 1 and 3, only Charbonnier loss [charbonnier1994two]

is used as the loss function. For Track 2, perceptual and adversarial loss functions are also used. The training patch size is

, randomly cropped from the original input images. They perform data augmentation, , rotation (), horizontal flip, and vertical flip. For the Track 1, they initialize the model from a variant trained for video super-resolution to shorten the training time. The models for the other two tracks are initialized from the model of Track 1. During the test phase, they test the proposed models with ensemble () testing, , rotation , flipping the input in four ways (none, horizontally, vertically, both horizontally and vertically) and averaging their outputs.

4.3 VUE Team

Tracks 1 and 3. In the fidelity tracks, the VUE Team proposes the methods based on BasicVSR [chan2021basicvsr], as shown in Figure 3. For Track 1, they propose a two-stage method. In stage-1, they train two BasicVSR models with different parameters followed by the self-ensemble strategy. Then, they fuse the two results using average sum. In stage-2, they train another BasicVSR model. For Track 3, they propose to tackle this problem by using VSR methods without the last upsampling layer. They train four BasicVSR models with different parameter settings followed by the self-ensemble strategy. Then, they average the four outputs as the final result.

(a) The method for Track 1.
(b) The method for Track 3.
(c) BasicVSR w/o pixel-shuffle.
Figure 3: The methods of the VUE Team for Tracks 1 and 3.
Figure 4: The proposed method of the VUE Team for Track 2.

Track 2. In Track 2, they propose a novel solution dubbed “Adaptive Spatial-Temporal Fusion of Two-Stage Multi-Objective Networks” [li2021VUE]. It is motivated by the fact that it is hard to design unified training objectives which are perceptual-friendly for enhancing regions with smooth content and regions with rich textures simultaneously. To this end, they propose to adaptively fuse the enhancement results from the networks trained with two different optimization objectives. As shown in Figure 4, the framework is designed with two stages. The first stage aims at obtaining the relatively good intermediate results with high fidelity. In this stage, a BasicVSR model is trained with Charbonnier loss [charbonnier1994two]. At the second stage, they train two BasicVSR models for different refinement purposes. One refined BasicVSR model (denoted as EnhanceNet2) is trained with


Another refined BasicVSR model (denoted as EnhanceNet1) is trained with the mere LPIPS loss [zhang2018unreasonable]. This way, EnhanceNet1 is good at recovering textures to satisfying human perception requirement but it can result in temporal flickering for smooth regions of videos, meanwhile EnhanceNet1 produces much more smooth results, especially, temporal flickering is well eliminated.

To overcome this issue, they devise a novel adaptive spatial-temporal fusion scheme. Specifically, the spatial-temporal mask generation module is proposed to produce spatial-temporal masks and it is used to fuse the two network outputs:


where is the generated mask for the -th frame, and are the -th output frames of EnhanceNet1 and EnhanceNet2, respectively. The mask is adaptively generated from , and

as follows. First, the variance map

is calculated from by:


where means the variance of . Then, they normalize the variance map in a temporal sliding window to generate the mask :


Intuitively, when a region is smooth, its local variance is small, otherwise, its local variance is large. Therefore, smooth region more relies on the output of EnhanceNet2 while the rich-texture region gets more recovered details from EnhanceNet1. With temporal sliding window, the temporal flickering effect is also well eliminated.

4.4 NOAHTCV Team

As show in Figure 5

, the input images includes three frames, , the current frame plus the previous and the next Peak Quality Frame (PQF). The first step consists in a shared feature extraction with a stack of residual blocks and subsequently an U-Net is used to jointly predict the individual offsets for each of the three inputs. Such offsets are then used to implicitly align and fuse the features. Note that, there is no loss used as supervision for this step. After the initial feature extraction and alignment, they use a multi-head U-Net with shared weights to process each input feature, and at each scale of the encoder and decoder, they fuse the U-Net features with scale-dependant deformable convolutions, which are denoted in black in Figure 

5. The output features of the U-Net are fused for a final time, and the output fused features are finally processed by a stack of residual blocks to predict the final output. This output is in fact a residual compression information which is added to the input frame to produce the enhanced output frame. The models utilized for all three tracks are the same. The difference is the loss function, , they use the loss for Tracks 1 and 3, and use GAN Loss + Perceptual loss + loss for Track 2.

Figure 5: The proposed method of the NOAHTCV Team.

4.5 MT.MaxClear Team

The proposed model is based on EDVR [wang2019edvr], which uses the deformable convolution to align features between neighboring frames and the reference frame, and then combines all aligned frame features to reconstruct the reference frame. The deformable convolution module in EDVR is difficult to train due to the unstable of DCN offset. They propose to add two DCN offset losses to regularize the deformable convolution module which makes the training of DCN offset much more stable. They use Charbonnier penalty loss [charbonnier1994two], DCN offsets Total Variation loss and DCN offsets Variation loss to train the model. The Charbonnier penalty loss is more robust than loss. DCN offsets Total Variation loss encourages the predicted DCN offsets are smooth in spatial space. DCN offsets Variation loss encourages the predicted DCN offsets between different channels do not deviate too much from the offsets mean. The training of DCN is much more stable due to the aforementioned two offsets losses, but the EDVR model performs better if the loss weights of DCN offsets Total Variation loss and DCN offsets Variation loss gradually decays to zero during training. In Track 2, they add the sharpening operation on the enhanced frames for better visual perception.

4.6 Shannon Team

Figure 6: The proposed generator of the Shannon Team.

The Shannon Team introduces a disentangled attention for compression artifact analysis. Unlike previous works, they propose to address the problem of artifact reduction from a new perspective: disentangle complex artifacts by a disentangled attention mechanism. Specifically, they adopt a multi-stage architecture in which early stage also provides a disentangled attention. Their key insight is that there are various types of artifacts created by video compression, some of which result in significant blurring effect in the reconstructed signals, and some of which generate artifacts, such as blocking and ringing. Algorithms could be either too aggressive and amplify erroneous high-frequency components, or too conservative and tend to smooth over ambiguous components, both resulting in bad cases that seriously affect subjective visual impression. The proposed disentangled attention aims to reduce these bad cases.

In Track 2, they use the LPIPS loss and only feed the high-frequency components to the discriminator. Before training the model, they analyze the quality fluctuation among frames [yang2018multi] and train the model from-easy-to-hard. To generate the attention map, they use the supervised attention module proposed proposed in [mehri2021mprnet]. The overall structure of the proposed generator is shown in Figure 6

. The discriminator is simply composed of several convolutional layer-ReLU-strided convolutional layers blocks, and its final output is a 4×4 confidence map.

Let denote the low-pass filtering, which is implemented in a differentiable manner with Kornia [riba2020kornia]. They derive the supervised information for attention map:


where denotes signum function that extracts the sign of a given pixel value; is the element-wise product, and refers to the output, and refers to the compressed input.

4.7 Block2Rock Noah-Hisilicon Team

Figure 7: The illustration of the proposed method of the Block2Rock Noah-Hisilicon Team.

This team makes a trade-off between spatial and temporal sizes in favor of the latter by performing collaborative CNN-based restoration of square patches extracted by block matching algorithm, which finds correlated areas across consequent frames. Due to performance concerns, the proposed block matching realization is trivial: for each patch at the reference frame, they search for and extract a single closest patch from each other frame in a sequence, based on squared distance:


Here is a linear operator that crops patch which top-left corner is located at pixel coordinates of the canvas. As it is shown for example in [google_hdr], the search for the closest patch in (10

) requires a few pointwise operations and two convolutions (one of which is a box filter), which could be done efficiently in the frequency domain based on convolution theorem. The resulted patches are then stacked and passed to a CNN backbone, which outputs a single enhanced version of the reference patch. The overall process is presented in Figure 


(a) Overall architecture
(b) Model architecture
(c) ResUNet architecture
Figure 8: The proposed method of Gogoing Team.

Since the total number of pixels being processed by CNN depends quadratically on spatial size and linearly on the input’s temporal size, they propose to use inference on small patches of size pixels to decrease the spatial size of backbone inputs. For example, the two times decrease in height and width allows increasing temporal dimension by a factor of four. With existing well-performing CNNs, this fact allows the temporal dimension to increase up to frames since such models are designed to be trained on patches with spatial sizes of more than pixels (typically 128 pixels).

In the solution, they use the EDVR network [wang2019edvr] as a backbone, and the RRDB network [rrdb] acted as a baseline. For EDVR, they stacke patches in a separate dimension, while for RRDB, they stacke patches in channel dimension. Reference patch was always the first in a stack.

For training network weights, they use the

distance between output and target as an objective to minimize through back-propagation and stochastic gradient descent. They use the Adam optimizer 

[kingma2014adam] with learning rate increased from zero to during a warmup period, which then was gradually decreased by a factor of

after each epoch. The total number of epochs was


unique batches passed to the network during each one. To stabilize the training and prevent divergence, they use the adaptive gradient clipping technique with weight

, as proposed in [agc].

4.8 Gogoing Team

The overall structure adopts a two-stage model, as shown in the Figure 8-(a). As Figure 8-(b) shows, for the temporal part, they use the temporal module in EDVR [wang2019edvr], that is PCD module and TSA module. The number of input frames is 7. In the spatial part, they combine the UNet [nah2020ntire] and the residual attention module [zhang2018image] to form ResUNet, as shown in Figure 8-(c). In the training phase, they use the 256256 RGB patchs from training set as input, and augment them with random horizontal flips and 90 rotations. The number of input frames is seven. All models are optimized by using the Adam [kingma2014adam] optimizer with mini-batches of size 12, with the learning rate being initialized to using CosineAnnealingRestartLR strategy. Loss function is the loss.

4.9 NJU-Vision Team

Figure 9: The proposed method of the NJU-Vision Team.

As shown in Figure 9, the NJU-Vision Team proposes a method utilizing a progressive deformable alignment module and a spatial-temporal attention based aggregation module, based on [wang2019edvr]. Data augmentation is also applied with training data augmentation by randomly flipping in horizontal and vertical orientations, and rotating at , , and , and evaluation ensemble by flipping in horizontal and vertical orientations, and rotating at , , and to obtain the averaged results.

(a) 3D–MultiGrid BackProjection network (MGBP–3D).
(b) Multiscale discriminator.
(c) Overlapping–patches inference strategy.
Figure 10: Network architectures of the BOE-IOT-AIBD Team.

4.10 BOE-IOT-AIBD Team

Tracks 1 and 3. Figure 10-(a) displays the diagram of the MGBP–3D network used in this challenge, which was proposed by the team members in [michelini2021multi]. The system uses two backprojection residual blocks that run recursively in five levels. Each level downsamples only space, not time, by the factor of 2. The Analysis and Synthesis modules convert an image into features space and vice–versa using single 3D–convolutional layers. The Upscaler and Downscaler modules are composed of single strided (transposed and conventional) 3D–convolutional layers. Every Upscaler and Downscaler module shares the same configuration in a given level but they do not share parameters. Small number of features are set at high resolution and they increase at lower resolutions to reduce the memory footprint in high resolution scales. In addition, they add a 1–channel noise video stream to the input that is only used for the Perceptual track. In the fidelity tracks, the proposed model is trained by the loss.

To process long video sequences they use the patch based approach from [michelini2021multi], in which they average the output of overlapping video patches taken from the compressed degraded input. First, they divide input streams into overlapping patches (of same size as training patches) as shown in Figure  10-(b); second, they multiply each output by weights set to a Hadamard window; and third, they average the results. In the experiments they use overlapping patches separated by 243 pixels in vertical and horizontal directions and one frames in time direction.

Track 2. Based on the model for Tracks 1 and 3, they add noise inputs to activate and deactivate the generation of artificial details for the Perceptual track. In MGBP–3D, they generate one channel of Gaussian noise concatenated to the bicubic upscaled input. The noise then moves to different scales in the Analysis blocks. This change allows using the overlapping patch solution with noise inputs, as it simply represent an additional channel in the input.

They further employ a discriminator shown in Figure 10-(c) to achieve adversarial training. The loss function used for Track 2 is a combination of the GAN loss, LPIPS loss and the loss. and as the outputs of the generator using noise amplitudes and , respectively. indicates the groundtruth. The loss function can be expressed as as follows:


Here, and the Relativistic GAN loss [jolicoeur2018relativistic], is given by:



is the output of the discriminator just before the sigmoid function, as shown in Figure 

10-(c). In (12), and are the sets of inputs to the discriminator, which contains multiple inputs with multiple scales, ,


4.11 (anonymous)

Figure 11: The proposed method of (anonymous).

Network. For enhancing the perceptual quality of heavily compressed videos, there are two main problems that need to be solved: spatial texture enhancement and temporal smoothing. Accordingly, in Track 2, they propose a multi-stage approach with specific designs for the above problems. Figure 11 depicts the overall framework of the proposed method, which consists of three processing stages. In stage I, they enhance the distorted textures of manifold objects in each compressed frame by the proposed Texture Imposition Module (TIM). In stage II, they suppress the flickering and discontinuity of the enhanced consecutive frames by a video alignment and enhancement network, , EDVR [wang2019edvr]. In stage III, they further enhance the sharpness of the enhanced videos by several classical techniques (opposite to learning-based network) .

In particular, TIM is based on the U-Net [unet] architecture to leverage the multi-level semantic guidance for texture imposition. In TIM, natural textures of different objects in compressed videos are assumed as different translation styles, which need to be learned and imposed; thus, they apply the affine transformations [stf] in the decoder path of the U-Net, to impose the various styles in a spatial way. The parameters of the affine transformations are learned from several convolutions with the input of guidance map from the encoder path of the U-Net. Stage II is based on the video deblurring model of EDVR, which consists of four modules, the PreDeblure, the PCD Align, the TSA fusion and the reconstruction module. In stage III, a combination of classical unsharp masking [deng2010generalized] and edge detection [sobel1972camera] methods are adopted to further enhance the sharpness of the video frames. In particular, they first obtain the difference map between the original and its Gaussian blurred images. Then, they utilize the Sobel operator [sobel1972camera] to detect the edges of original image to weight the difference map, and add the difference map to the original image.

(a) The overall architecture of the CVQENet.
(b) The architecture of the DPM.
Figure 12: The proposed method of the VIP&DJI Team for Track 1.
(a) The whole framework of the DUVE.
(b) The architecture of U-Net.
Figure 13: The proposed method of the VIP&DJI Team for Track 3.

Training. For stage I, TIM is supervised by three losses: Charbonnier loss (epsilon is set to be ) , VGG loss and Relativistic GAN loss . The overall loss function is defined as: . For stage II, they fine-tune the video deblurring model of EDVR with the training set of NTIRE, supervised by the default Charbonnier loss. Instead of the default training setting in [wang2019edvr], they first fine-tune the PreDeblur module with 80,000 iterations. Then they fine-tune the overall model with a small learning rate of for another 155,000 iterations.

4.12 VIP&DJI Team

4.12.1 Track 1

As shown in Figure 12-(a), the architecture of the proposed CVQENet consists of five parts, which are feature extraction module, inter-frame feature deformable alignment module, inter-frame feature temporal fusion module, decompression processing module and feature enhancement module. The input of CVQENet includes five consecutive compressed video frames , and the output is a restored middle frame that is as close as possible to the uncompressed middle frame :


where represents the set of all parameters of the CVQENet. Given a training dataset, the loss function is defined below to be minimized:


where denotes the loss. The following will introduce each module of CVQENet in detail.

Feature extraction module (FEM). The Feature extraction module contains one convolutional layer (Conv) to extract the shallow feature maps from the compressed video frames

and 10 stacked residual blocks without Batch Normalization (BN) layer to process the feature maps further.

Inter-frame feature deformable alignment module (FDAM). Next, for the feature maps extracted by the FEM, FDAM aligns the feature map corresponding to each frame to the middle frame that needs to be restored. It can be aligned based on optical flow, deformable convolution, 3D convolution, and other methods, and CVQENet uses the method based on deformable convolution. For simplicity, CNet uses the Pyramid, Cascading and Deformable convolutions (PCD) module proposed by EDVR [wang2019edvr] to align feature maps. The detailed module structure is shown in Figure12-(a). The PCD module aligns the feature map of each frame to the feature map of the middle frame . In the alignment process, is progressively convolved and down-sampled to obtain a small-scale feature maps , and then the align is processed from to in a coarse-to-fine manner. Align with respectively to obtain the aligned feature map .

Inter-frame feature temporal fusion module (FTFM). The FTFM is used to fuse the feature maps from each frame to a compact and informative feature map for further process. CVQENet directly uses the TSA module proposed by EDVR [wang2019edvr] for fusion, and the detailed structure is shown in Figure12-(a). The TSA module generates temporal attention maps through the correlation between frames and then performs temporal feature fusion through the convolutional layer. Then, the TSA module uses spatial attention to further enhance the feature map.

Decompression processing module (DPM). For the fused feature map, CVQENet uses the DPM module to remove artifacts caused by compression. Inspired by RBQE [xing2020early], DPM consists of a simple densely connected UNet, as shown in Figure12-(b). The cell contains Efficient Channel Attention (ECA) [wang2020eca] block, convolutional layer and residual blocks. The ECA block performs adaptive feature amplitude adjustment through the channel attention mechanism.

Feature quality enhancement module (FQEM). CVQENet add the output of DPM with , then enters them into the feature quality enhancement module. The shallow feature map contains a wealth of detailed information of the middle frame, which can help restore the middle frame. The FQEM contains 20 stacked residual blocks without Batch Normalization (BN) layer to enhance the feature map further and one convolutional layer (Conv) to generate the output frame image .

4.12.2 Track 3

Motivated by FastDVDnet [tassano2020fastdvdnet] and DRN [guo2020closed], they propose the DUVE network for compressed video enhancement for Track 3, and the whole framework is shown in Figure 13-(a). It can be seen that given five consecutive compressed frames , the goal of DUVE is to restore an uncompressed frame . Specifically, for five continuous input frames, each of the three consecutive images forms a group, so that the five images are overlapped into three groups , and . Then, the three groups are fed into Unet1 to get coarse restored feature maps , and , respectively. Considering the correlation between different frames and the current reconstruction frame , the two groups of coarse feature maps and

are filtered by nonlinear activation function

to get and . Next, , and are concatenated along the channel dimension, and then pass through channel reduction module to obtain fused coarse feature map . To further reduce compression artifacts, they apply UNet2 on to acquire more fine feature map . Finally, a quality enhancement module takes the fine feature map to achieve the restored frame . The detailed architecture of Unet1 and Unet2 is shown in Figure 13-(b). In the proposed method, the mere difference between Unet1 and Unet2 is the number of Residual Channel Attention Blocks (RCAB) [zhang2018image]. The loss is utilized as the loss function.

4.13 BLUEDOT Team

(a) The proposed framework.
(b) Intra-Frames Texture Transformer (IFTT).
(c) Attention fusion network in IFTT.
Figure 14: The proposed method of the BLUEDOT Team.

The proposed method is shown in Figure 14

, which is motivated by the idea that intra-frames usually have better video quality than inter-frames. It means that more information about the texture of the videos from intra-frames can be extracted. They built and trained a neural network built on EDVR 

[wang2019edvr] and TTSR [yang2020learning]. Relevances of all intra-frames in the video are measured and one frame of the high- est relevance with the current frame is embedded in the network. They carry out the two-stage training in the same network to obtain a video enhancement result with the restored intra-frame. In the first stage, the model is learned by low-quality intra-frames. Then, in the second stage, the model is trained with predicted intra-frames by fisrt-stage model.

4.14 HNU_CVers Team

The HNU_CVers Team proposes the patch-based heavy compression recovery models for Track 1.

Single-frame residual channel-attention network. First, they delete the upsampling module from [zhang2018image] and add a skip connection to build a new model. Different from [zhang2018image], the RG (Residual Group) number is set as 5 and there are 16 residual blocks [lim2017enhanced] in each RG. Each residual block is composed of convolutional layers and ReLU with 64 feature maps. The single-frame model architecture is shown in Figure 15-(a), called RCAN_A. The model is trained with the loss.

Multi-frame residual channel-attention video network. They further make the model compact by five consecutive frames of images which are enhanced by RCAN_A. The multi-frame model architecture is shown in Figure 15-(b). Five consecutive frames are stained with different colors in Figure 15-(b) after enhanced by RCAN_A. In order to mine consecutive frames information, they combine the central frame with each frame. For aligning, they designe the [conv (64 features) + ReLU + Resbolcks (64 features) 5] network as the align network with the shared parameters to each combination. Immediately after it, temporal and spatial attention are fused [wang2019edvr]. After getting a single frame feature map colored yellow (shown in Figure 15-(b), they adopt another model called RCAN_B, which has the same structure as RCAN_A. Finally, the restored RGB image is obtained through a convolution layer. The model is also trained with the loss.

Patch Integration Method (PIM). They further propose a patch-based fusion model to strengthen the reconstruction ability of the multi-frame model. The motivation for designing PIM is to mine the reconstruction ability of the model from part to the whole. For a small patch, the reconstruction ability of the model at the center will exceed the reconstruction ability at the edge. Therefore, they propose feed the overlapping patches to the proposed network, In the reconstructed patches, they remove the edges that overlap with the neighboring patches and only keep the high-confidence part in the center.

4.15 McEnhance Team

(a) Single-frame Residual Channel-Attention Network (RCAN_A)
(b) Multi-frame Residual Channel-Attention Video Network (RCVN)
Figure 15: The proposed method of the HNU_CVers Team.
Figure 16: The proposed method of the McEnhance Team.

The McEnhance Team combines video super-resolution technology [deng2020spatio, wang2019edvr] with multi-frame enhancement [yang2018multi, guan2019mfqe] to create a new end-to-end network, as illustrated in Figure 16. First, they choose the current frame and it’s neighbor peak Frames as the input data. Then, they feed them to the deformable convolution network to align. As a result, complementary information from both target and reference frames can be fused with in the operation. In the following, they feed them separately to the QE module [deng2020spatio] and the Temporal and Spatial Attention (TSA) [wang2019edvr] network. Finally, they put the two residual frames on the raw target frame. There are two steps in training stage. First, they calculate the PSNR of each frame in training set and make labels of peak PSNR frames. Secondly, they send the current frame and two neighbor peak PSNR frames to the net.

4.16 Ivp-tencent

Figure 17: The proposed BRNet of the Ivp-tencent Team.

As Figure 17 shows, the Ivp-tencent Team proposes a Block Removal Network (BRNet) to reduce the block artifacts in compressed video for quality enhancement. Inspired by EDSR [lim2017enhanced] and FFDNet [zhang2018ffdnet], the proposed BRNet first uses a mean shift module (Mean Shift) to normalize the input frame, and then adopts a reversible down-sampling operation (Pixel Unshuffle) to process the frame, which splits the compressed frame into four down-sampled sub-frames. Then, the sub-frames are fed into a convolutional network shown in Figure 17, in which they use eight residual blocks. Finally, they use an up-sampling operation (Pixel Shuffle) and a mean shift module to reconstruct the enhanced frame. Note that, the up-sampling operation (Pixel Shuffle) is the inverse operation of the down-sampling operation (Pixel Unshuffle). During the training phase, they crop the given compressed images to and feed to the network by batch size of 64. The Adam [kingma2014adam] algorithm is adopted to optimize loss, and learning rate is set to . The model is trained for 100,000 epochs.

The proposed BRNet achieves higher efficiency compared with EDSR and FFDNet. The reason is two-fold: First, the input frame is sub-sampled into several sub-frames as inputs to the network. While maintaining the quality performance, the network parameters are effectively reduced and the receiving field of the network is increased. Second, by removing the batch normalization layer of the residual blocks, about 40% of the memory usage can be saved during training.


We thank the NTIRE 2021 sponsors: Huawei, Facebook Reality Labs, Wright Brothers Institute, MediaTek, and ETH Zurich (Computer Vision Lab). We also thank the volunteers for the perceptual experiment of Track 2.

Appendix: Teams and affiliations

NTIRE 2021 Team


NTIRE 2021 Challenge on Quality Enhancement of Compressed Video


Ren Yang (ren.yang@vision.ee.ethz.ch),

Radu Timofte (radu.timofte@vision.ee.ethz.ch)


Computer Vision Lab, ETH Zurich, Switzerland

Bilibili AI & FDU Team


Tracks 1 and 3: Spatiotemporal Model with Gated Fusion for Compressed Video Artifact Reduction

Track 2: Perceptual Spatiotemporal Model with Gated Fusion for Compressed Video Artifact Reduction


Jing Liu (liujing04@bilibili.com), Yi Xu (yxu17@fudan.edu.cn), Xinjian Zhang, Minyi Zhao, Shuigeng Zhou


Bilibili Inc.; Fudan University, Shanghai, China

NTU-SLab Team


BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment


Kelvin C.K. Chan (chan0899@e.ntu.edu.sg), Shangchen Zhou, Xiangyu Xu, Chen Change Loy


S-Lab, Nanyang Technological University, Singapore

VUE Team


Tracks 1 and 3: Leveraging off-the-shelf BasicVSR for Video Enhancement

Track 2: Adaptive Spatial-Temporal Fusion of Two-Stage Multi-Objective Networks


Xin Li (lixin41@baidu.com), He Zheng (zhenghe01@baidu.com), Fanglong Liu, Lielin Jiang, Qi Zhang, Dongliang He, Fu Li, Qingqing Dang


Department of Computer Vision Technology (VIS), Baidu Inc., Beijing, China



Multi-scale network with deformable temporal fusion for compressed video restoration


Fenglong Song (songfenglong@huawei.com), Yibin Huang, Matteo Maggioni, Zhognqian Fu, Shuai Xiao, Cheng li, Thomas Tanay


Huawei Noah’s Ark Lab, Huawei Technologies Co., Ltd.

MT.MaxClear Team


Enhanced EDVRNet for Quality Enhanced of Heavily Compressed Video


Wentao Chao (cwt1@meitu.com), Qiang Guo, Yan Liu, Jiang Li, Xiaochao Qu


MTLab, Meitu Inc., Beijing, China

Shannon Team


Disentangled Attention for Enhancement of Compressed Videos


Dewang Hou (dewh@pku.edu.cn), Jiayu Yang, Lyn Jiang, Di You, Zhenyu Zhang, Chong Mou


Peking university, Shenzhen, China; Tencent, Shenzhen, China

Block2Rock Noah-Hisilicon Team


Long Temporal Block Matching for Enhancement of Uncompressed Videos


Iaroslav Koshelev (Iaroslav.Koshelev@skoltech.ru), Pavel Ostyakov, Andrey Somov, Jia Hao, Xueyi Zou


Skolkovo Institute of Science and Technology, Moscow, Russia; Huawei Noah’s Ark Lab; HiSilicon (Shanghai) Technologies CO., LIMITED, Shanghai, China

Gogoing Team


Two-stage Video Enhancement Network for Different QP Frames


Shijie Zhao (zhaoshijie.0526@bytedance.com), Xiaopeng Sun, Yiting Liao, Yuanzhi Zhang, Qing Wang, Gen Zhan, Mengxi Guo, Junlin Li


ByteDance Ltd., Shenzhen, China

NJU-Vision Team


Video Enhancement with Progressive Alignment and Data Augmentation


Ming Lu (luming@smail.nju.edu.cn), Zhan Ma


School of Electronic Science and Engineering, Nanjing University, China



Fully 3D–Convolutional MultiGrid–BackProjection Network


Pablo Navarrete Michelini (pnavarre@boe.com.cn)


BOE Technology Group Co., Ltd., Beijing, China



Track 1: CVQENet: Deformable Convolution-based Compressed Video Quality Enhancement Network

Track 3: DUVE: Compressed Videos Enhancement with Double U-Net


Hai Wang (wanghai19@mails.tsinghua.edu.cn), Yiyun Chen, Jingyu Guo, Liliang Zhang, Wenming Yang


Tsinghua University, Shenzhen, China; SZ Da-Jiang Innovations Science and Technology Co., Ltd., Shenzhen, China



Intra-Frame texture transformer Network for compressed video enhancement


Sijung Kim (jun.kim@blue-dot.io), Syehoon Oh


Bluedot, Seoul, Republic of Korea

HNU_CVers Team


Patch-Based Multi-Frame Residual Channel-Attention Networks For Video Enhancement


Yucong Wang (1401121556@qq.com), Minjie Cai


College of Computer Science and Electronic Engineering, Hunan University, China

McEnhance Team


Parallel Enhancement Net


Wei Hao (haow6@mcmaster.ca), Kangdi Shi, Liangyan Li, Jun Chen


McMaster University, Ontario, Canada

Ivp-tencent Team


BRNet: Block Removal Network


Wei Gao (gaowei262@pku.edu.cn), Wang Liu, Xiaoyu Zhang, Linjie Zhou, Sixin Lin, Ru Wang


School of Electronic and Computer Engineering, Shenzhen Graduate School, Peking University, China; Peng Cheng Laboratory, Shenzhen, China; Tencent, Shenzhen, China