Rate-Distortion Optimized Encoding for Deep Image Compression

Deep-learned variational auto-encoders (VAE) have shown remarkable capabilities for lossy image compression. These neural networks typically employ non-linear convolutional layers for finding a compressible representation of the input image. Advanced techniques such as vector quantization, context-a...

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Autores principales:	Michael Schafer, Sophie Pientka, Jonathan Pfaff, Heiko Schwarz, Detlev Marpe, Thomas Wiegand
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Deep image compression variational auto-encoders rate-distortion optimized encoding non-linear transform coding Electric apparatus and materials. Electric circuits. Electric networks TK452-454.4
Acceso en línea:	https://doaj.org/article/bb87eff80345459faf4e5ddac9532e1c
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id	oai:doaj.org-article:bb87eff80345459faf4e5ddac9532e1c
record_format	dspace
spelling	oai:doaj.org-article:bb87eff80345459faf4e5ddac9532e1c2021-11-23T00:02:21ZRate-Distortion Optimized Encoding for Deep Image Compression2644-122510.1109/OJCAS.2021.3124995https://doaj.org/article/bb87eff80345459faf4e5ddac9532e1c2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9623337/https://doaj.org/toc/2644-1225Deep-learned variational auto-encoders (VAE) have shown remarkable capabilities for lossy image compression. These neural networks typically employ non-linear convolutional layers for finding a compressible representation of the input image. Advanced techniques such as vector quantization, context-adaptive arithmetic coding and variable-rate compression have been implemented in these auto-encoders. Notably, these networks rely on an end-to-end approach, which fundamentally differs from hybrid, block-based video coding systems. Therefore, signal-dependent encoder optimizations have not been thoroughly investigated for VAEs yet. However, rate-distortion optimized encoding heavily determines the compression performance of state-of-the-art video codecs. Designing such optimizations for non-linear, multi-layered networks requires to understand the relationship between the quantization, the bit allocation of the features and the distortion. Therefore, this paper examines the rate-distortion performance of a variable-rate VAE. In particular, one demonstrates that the trained encoder network typically finds features with a near-optimal bit allocation across the channels. Furthermore, one approximates the relationship between distortion and quantization by a higher-order polynomial, whose coefficients can be robustly estimated. Based on these considerations, the authors investigate an encoding algorithm for the Lagrange optimization, which significantly improves the coding efficiency.Michael SchaferSophie PientkaJonathan PfaffHeiko SchwarzDetlev MarpeThomas WiegandIEEEarticleDeep image compressionvariational auto-encodersrate-distortion optimized encodingnon-linear transform codingElectric apparatus and materials. Electric circuits. Electric networksTK452-454.4ENIEEE Open Journal of Circuits and Systems, Vol 2, Pp 633-647 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Deep image compression variational auto-encoders rate-distortion optimized encoding non-linear transform coding Electric apparatus and materials. Electric circuits. Electric networks TK452-454.4
spellingShingle	Deep image compression variational auto-encoders rate-distortion optimized encoding non-linear transform coding Electric apparatus and materials. Electric circuits. Electric networks TK452-454.4 Michael Schafer Sophie Pientka Jonathan Pfaff Heiko Schwarz Detlev Marpe Thomas Wiegand Rate-Distortion Optimized Encoding for Deep Image Compression
description	Deep-learned variational auto-encoders (VAE) have shown remarkable capabilities for lossy image compression. These neural networks typically employ non-linear convolutional layers for finding a compressible representation of the input image. Advanced techniques such as vector quantization, context-adaptive arithmetic coding and variable-rate compression have been implemented in these auto-encoders. Notably, these networks rely on an end-to-end approach, which fundamentally differs from hybrid, block-based video coding systems. Therefore, signal-dependent encoder optimizations have not been thoroughly investigated for VAEs yet. However, rate-distortion optimized encoding heavily determines the compression performance of state-of-the-art video codecs. Designing such optimizations for non-linear, multi-layered networks requires to understand the relationship between the quantization, the bit allocation of the features and the distortion. Therefore, this paper examines the rate-distortion performance of a variable-rate VAE. In particular, one demonstrates that the trained encoder network typically finds features with a near-optimal bit allocation across the channels. Furthermore, one approximates the relationship between distortion and quantization by a higher-order polynomial, whose coefficients can be robustly estimated. Based on these considerations, the authors investigate an encoding algorithm for the Lagrange optimization, which significantly improves the coding efficiency.
format	article
author	Michael Schafer Sophie Pientka Jonathan Pfaff Heiko Schwarz Detlev Marpe Thomas Wiegand
author_facet	Michael Schafer Sophie Pientka Jonathan Pfaff Heiko Schwarz Detlev Marpe Thomas Wiegand
author_sort	Michael Schafer
title	Rate-Distortion Optimized Encoding for Deep Image Compression
title_short	Rate-Distortion Optimized Encoding for Deep Image Compression
title_full	Rate-Distortion Optimized Encoding for Deep Image Compression
title_fullStr	Rate-Distortion Optimized Encoding for Deep Image Compression
title_full_unstemmed	Rate-Distortion Optimized Encoding for Deep Image Compression
title_sort	rate-distortion optimized encoding for deep image compression
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/bb87eff80345459faf4e5ddac9532e1c
work_keys_str_mv	AT michaelschafer ratedistortionoptimizedencodingfordeepimagecompression AT sophiepientka ratedistortionoptimizedencodingfordeepimagecompression AT jonathanpfaff ratedistortionoptimizedencodingfordeepimagecompression AT heikoschwarz ratedistortionoptimizedencodingfordeepimagecompression AT detlevmarpe ratedistortionoptimizedencodingfordeepimagecompression AT thomaswiegand ratedistortionoptimizedencodingfordeepimagecompression
_version_	1718417398058450944

Rate-Distortion Optimized Encoding for Deep Image Compression

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