Learned Image Compression with Generalized Octave Convolution and Cross-Resolution Parameter Estimation

09/07/2022
by   Haisheng Fu, et al.
9

The application of the context-adaptive entropy model significantly improves the rate-distortion (R-D) performance, in which hyperpriors and autoregressive models are jointly utilized to effectively capture the spatial redundancy of the latent representations. However, the latent representations still contain some spatial correlations. In addition, these methods based on the context-adaptive entropy model cannot be accelerated in the decoding process by parallel computing devices, e.g. FPGA or GPU. To alleviate these limitations, we propose a learned multi-resolution image compression framework, which exploits the recently developed octave convolutions to factorize the latent representations into the high-resolution (HR) and low-resolution (LR) parts, similar to wavelet transform, which further improves the R-D performance. To speed up the decoding, our scheme does not use context-adaptive entropy model. Instead, we exploit an additional hyper layer including hyper encoder and hyper decoder to further remove the spatial redundancy of the latent representation. Moreover, the cross-resolution parameter estimation (CRPE) is introduced into the proposed framework to enhance the flow of information and further improve the rate-distortion performance. An additional information-fidelity loss is proposed to the total loss function to adjust the contribution of the LR part to the final bit stream. Experimental results show that our method separately reduces the decoding time by approximately 73.35 that of state-of-the-art learned image compression methods, and the R-D performance is still better than H.266/VVC(4:2:0) and some learning-based methods on both PSNR and MS-SSIM metrics across a wide bit rates.

READ FULL TEXT

page 1

page 5

page 6

page 7

page 10

page 11

page 12

research
02/24/2020

Generalized Octave Convolutions for Learned Multi-Frequency Image Compression

Learned image compression has recently shown the potential to outperform...
research
07/17/2020

Channel-wise Autoregressive Entropy Models for Learned Image Compression

In learning-based approaches to image compression, codecs are developed ...
research
03/29/2021

Checkerboard Context Model for Efficient Learned Image Compression

For learned image compression, the autoregressive context model is prove...
research
06/23/2022

Universal Learned Image Compression With Low Computational Cost

Recently, learned image compression methods have developed rapidly and e...
research
02/18/2023

Multistage Spatial Context Models for Learned Image Compression

Recent state-of-the-art Learned Image Compression methods feature spatia...
research
12/22/2018

Learned Scalable Image Compression with Bidirectional Context Disentanglement Network

In this paper, we propose a learned scalable/progressive image compressi...
research
03/25/2022

RD-Optimized Trit-Plane Coding of Deep Compressed Image Latent Tensors

DPICT is the first learning-based image codec supporting fine granular s...

Please sign up or login with your details

Forgot password? Click here to reset