Video Quality Assessment with Texture Information Fusion for Streaming Applications
share
The rise of video streaming applications has increased the demand for Video Quality Assessment (VQA). In 2016, Netflix introduced VMAF, a full reference VQA metric that strongly correlates with perceptual quality, but its computation is time-intensive. This paper proposes a Discrete Cosine Transform (DCT)-energy-based VQA with texture information fusion (VQ-TIF ) model for video streaming applications that predicts VMAF for the reconstructed video compared to the original video. VQ-TIF extracts Structural Similarity (SSIM) and spatio-temporal features of the frames from the original and reconstructed videos, fuses them using a Long Short-Term Memory (LSTM)-based model to estimate VMAF. Experimental results show that VQ-TIF estimates VMAF with a Pearson Correlation Coefficient (PCC) of 0.96 and a Mean Absolute Error (MAE) of 2.71, on average, compared to the ground truth VMAF scores. Additionally, VQ-TIF estimates VMAF at a rate of 9.14 times faster than the state-of-the-art VMAF implementation and a 89.44 an Ultra HD (2160p) display resolution.
READ FULL TEXT