TY - GEN
T1 - Description of challenge proposal by NCTU
T2 - 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019
AU - Chang, Chih Peng
AU - Alexandre, David
AU - Peng, Wen Hsiao
AU - Hang, Hsueh-Ming
N1 - Publisher Copyright:
© 2019 IEEE Computer Society. All rights reserved.
PY - 2019/6
Y1 - 2019/6
N2 - This paper describes the technology proposal by NCTU for learning-based image compression. The selected technologies include an autoencoder that incorporates (1) a principal component analysis (PCA) layer for energy compaction, (2) a uniform, scalar quantizer for lossy compression, (3) a context-adaptive bitplane coder for entropy coding, and (4) a soft-bit-based rate estimator. The PCA layer includes 1×1 eigen kernels derived from the sample covariance of co-located feature samples across channels. The bitplane coder compresses PCA-transformed feature samples based on their quantized, fixed-point representations, of which the soft bits provide a differentiable approximation for context-adaptive rate estimation. The training of our compression system proceeds in two alternating phases: one for updating the rate estimator and the other for fine tuning the autoencoder regularized by the rate estimator. The proposed method outperforms BPG in terms of both PSNR and MS-SSIM. Several bug fixes have been made since the submission of our decoder. This paper presents the up-to-date results.
AB - This paper describes the technology proposal by NCTU for learning-based image compression. The selected technologies include an autoencoder that incorporates (1) a principal component analysis (PCA) layer for energy compaction, (2) a uniform, scalar quantizer for lossy compression, (3) a context-adaptive bitplane coder for entropy coding, and (4) a soft-bit-based rate estimator. The PCA layer includes 1×1 eigen kernels derived from the sample covariance of co-located feature samples across channels. The bitplane coder compresses PCA-transformed feature samples based on their quantized, fixed-point representations, of which the soft bits provide a differentiable approximation for context-adaptive rate estimation. The training of our compression system proceeds in two alternating phases: one for updating the rate estimator and the other for fine tuning the autoencoder regularized by the rate estimator. The proposed method outperforms BPG in terms of both PSNR and MS-SSIM. Several bug fixes have been made since the submission of our decoder. This paper presents the up-to-date results.
UR - http://www.scopus.com/inward/record.url?scp=85113881756&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85113881756
T3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
SP - 4321
EP - 4325
BT - Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019
PB - IEEE Computer Society
Y2 - 16 June 2019 through 20 June 2019
ER -