Green image codec: a lightweight learning-based image coding method

Yifan Wang; Zhanxuan Mei; Qingyang Zhou; Ioannis Katsavounidis; C.-C. Jay Kuo

doi:10.1117/12.2633475

3 October 2022 Green image codec: a lightweight learning-based image coding method

Yifan Wang, Zhanxuan Mei, Qingyang Zhou, Ioannis Katsavounidis, C.-C. Jay Kuo

Proceedings Volume 12226, Applications of Digital Image Processing XLV; 122260A (2022) https://doi.org/10.1117/12.2633475
Event: SPIE Optical Engineering + Applications, 2022, San Diego, California, United States

Abstract

Image compression has experienced a new revolution with the success of deep learning, which yields superior rate-distortion (RD) performance against traditional codecs. Yet, high computational complexity and energy consumption are the main bottlenecks that hinder the practical applicability of deep-learning-based (DL-based) codecs. Inspired by the neural network’s hierarchic structure yet with lower complexity, we propose a new lightweight image coding framework and name it the ”Green Image Codec” (GIC) in this work. First, GIC down-samples an input image into several spatial resolutions from fine-to-coarse grids and computes image residuals between two adjacent grids. Then, it encodes the coarsest content, interpolates content from coarse-tofine grids, encodes residuals, and adds residuals to interpolated images for reconstruction. All coding steps are implemented by vector quantization while all interpolation steps are conducted by the Lanczos interpolation. To facilitate VQ codebook training, the Saab transform is applied for energy compaction and, thus, dimension reduction. A simple rate-distortion optimization (RDO) is developed to help select the coding parameters. GIC yields an RD performance that is comparable with BPG at significantly lower complexity.

Conference Presentation

Citation Download Citation

Yifan Wang, Zhanxuan Mei, Qingyang Zhou, Ioannis Katsavounidis, and C.-C. Jay Kuo "Green image codec: a lightweight learning-based image coding method", Proc. SPIE 12226, Applications of Digital Image Processing XLV, 122260A (3 October 2022); https://doi.org/10.1117/12.2633475

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available