Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/106296
Title: Signal-independent separable KLT by offline training for video coding
Authors: Fan, Kui
Wang, Ronggang
Lin, Weisi
Duan, Ling-Yu
Gao, Wen
Keywords: KLT
Transform
DRNTU::Engineering::Computer science and engineering
Issue Date: 2019
Source: Fan, K., Wang, R., Lin, W., Duan, L.-Y., & Gao, W. (2019). Signal-independent separable KLT by offline training for video coding. IEEE Access, 7, 33087-33093. doi:10.1109/ACCESS.2019.2903734
Series/Report no.: IEEE Access
Abstract: After the works on High Efficiency Video Coding (HEVC) standard, the standard organizations continued to study the next generation of video coding standard, named Versatile Video Coding (VVC). The compression capacity of the VVC standard is expected to be substantially improved relative to the current HEVC standard by evolving the potential coding tools greatly. Transform is a key technique for compression efficiency, and core experiment 6 (CE6) in JVET is established to explore the transform-related coding tools. In this paper, we propose a novel signal-independent separable transform based on the Karhunen-Loève transform (KLT) to improve the efficiency of both intra and inter residual coding. In the proposed method, the drawbacks of the traditional KLT are addressed. A group of mode-independent intra transform matrices is calculated from abundant intra residual samples of all intra modes, while the inter separable KLT matrices are trained with the residuals that cannot be efficiently processed by the discrete cosine transform type II (DCT-II). The separable KLT is developed as an additional transform type apart from DCT-II. The experimental results show that the proposed method can achieve 2.7% and 1.5% bitrate saving averagely under All Intra and Random Access configurations on top of the reference software of VVC (VTM-1.1). In addition, the consistent performance improvement on test set also validates the property of signal independency and the strong generalization capacity of the proposed separable KLT.
URI: https://hdl.handle.net/10356/106296
http://hdl.handle.net/10220/48882
DOI: 10.1109/ACCESS.2019.2903734
Rights: © 2019 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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