A hybrid image codec with learned residual coding

Wei Cheng Lee, Hsueh Ming Hang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We propose a three-layer image compression system consisting of a base-layer VVC (intra) codec, a learning-based residual layer codec, and a learnable hyperprior. This proposal (Team: NCTU-Commlab) is submitted to the Challenge on Learned Image Compression (CLIC) in March 2020. Our contribution is developing a data fusion attention module and integrating several known components together to form an efficient image codec, which has a higher compression performance than the standard VVC coding scheme. Unlike the conventional residual image coding, both our encoder and decoder take inputs also from the base-layer output. Also, we construct a refinement neural network to merge the residual-layer decoded residual image and the base-layer decoded image together to form the final reconstructed image. We tested two autoencoder structures for the encoder and decoder, namely, CNN with GDN [5], [6], and the generalized octave CNN [4]. Our results show that the transmitted latent representations are very efficient in coding the residuals because the object boundary information can be provided by the proposed spatial attention module. The experiments indicate that the proposed system achieves better performance than the single-layer VVC at both PSNR and subjective quality at around 0.15 bit-per-pixel.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020
PublisherIEEE Computer Society
Pages570-574
Number of pages5
ISBN (Electronic)9781728193601
DOIs
StatePublished - Jun 2020
Event2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020 - Virtual, Online, United States
Duration: 14 Jun 202019 Jun 2020

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Volume2020-June
ISSN (Print)2160-7508
ISSN (Electronic)2160-7516

Conference

Conference2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020
CountryUnited States
CityVirtual, Online
Period14/06/2019/06/20

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