Online-learned graph transforms for adaptive block size intra-predictive coding

Wen-Yang Lu; Eduardo Pavez; Antonio Ortega; Xin Zhao; Shan Liu

doi:10.1117/12.3034041

30 September 2024 Online-learned graph transforms for adaptive block size intra-predictive coding

Wen-Yang Lu, Eduardo Pavez, Antonio Ortega, Xin Zhao, Shan Liu

Author Affiliations +

Proceedings Volume 13137, Applications of Digital Image Processing XLVII; 131371B (2024) https://doi.org/10.1117/12.3034041
Event: Optical Engineering + Applications, 2024, San Diego, California, United States

Abstract

Current video coding standards, including H.264/AVC, HEVC, and VVC, utilize discrete cosine transform (DCT), discrete sine transform (DST), to decorrelate the intra-prediction residuals. However, these transforms often face challenges in effectively decorrelating signals with complex, non-smooth, and non-periodic structures. Even in smooth areas, an abrupt transition (due to noise or prediction artifacts) can limit their effectiveness. This paper presents a novel block-adaptive separable path graph-based transform (GBT) that is particularly adept at handling such signals. This new method focuses on adaptively modifying the block size and learning GBT to enhance the performance. The GBT is learned in an online scenario using sequential K-means clustering, where each available block size has K clusters and K GBT kernels. This approach allows the GBT to be dynamically learned for the current block based on previously reconstructed areas with same block size and similar characteristics. Our evaluation, integrating this method with H.264/AVC intra-coding tools, shows significant improvement over the traditional H.264/AVC DCT in processing high-resolution natural images.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Wen-Yang Lu, Eduardo Pavez, Antonio Ortega, Xin Zhao, and Shan Liu "Online-learned graph transforms for adaptive block size intra-predictive coding", Proc. SPIE 13137, Applications of Digital Image Processing XLVII, 131371B (30 September 2024); https://doi.org/10.1117/12.3034041

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