Variable-Rate Deep Image Compression through Spatially-Adaptive Feature Transform

08/21/2021
by   Myungseo Song, et al.
1

We propose a versatile deep image compression network based on Spatial Feature Transform (SFT arXiv:1804.02815), which takes a source image and a corresponding quality map as inputs and produce a compressed image with variable rates. Our model covers a wide range of compression rates using a single model, which is controlled by arbitrary pixel-wise quality maps. In addition, the proposed framework allows us to perform task-aware image compressions for various tasks, e.g., classification, by efficiently estimating optimized quality maps specific to target tasks for our encoding network. This is even possible with a pretrained network without learning separate models for individual tasks. Our algorithm achieves outstanding rate-distortion trade-off compared to the approaches based on multiple models that are optimized separately for several different target rates. At the same level of compression, the proposed approach successfully improves performance on image classification and text region quality preservation via task-aware quality map estimation without additional model training. The code is available at the project website: https://github.com/micmic123/QmapCompression

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 2

page 8

page 14

page 15

page 16

page 17

page 18

page 19

09/11/2019

Variable Rate Deep Image Compression With a Conditional Autoencoder

In this paper, we propose a novel variable-rate learned image compressio...
12/11/2019

Variable Rate Deep Image Compression with Modulated Autoencoder

Variable rate is a requirement for flexible and adaptable image and vide...
12/16/2020

CompositeTasking: Understanding Images by Spatial Composition of Tasks

We define the concept of CompositeTasking as the fusion of multiple, spa...
02/18/2020

Variable-Bitrate Neural Compression via Bayesian Arithmetic Coding

Deep Bayesian latent variable models have enabled new approaches to both...
10/09/2021

Deep Joint Source-Channel Coding for Wireless Image Transmission with Adaptive Rate Control

We present a novel adaptive deep joint source-channel coding (JSCC) sche...
12/27/2016

Semantic Perceptual Image Compression using Deep Convolution Networks

It has long been considered a significant problem to improve the visual ...
03/18/2020

Object-Based Image Coding: A Learning-Driven Revisit

The Object-Based Image Coding (OBIC) that was extensively studied about ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.