A low complexity alf based on inter-channel correlation between chroma and luma in HEVC

Wei Jing Chen, Lei Sun, Lei Gu, Zhen Yu Liu, Takeshi Ikenaga

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The adaptive loop filter (ALF) is a Wiener algorithm based technology which is applied to the reconstructed frame to improve the image quality in high-efficiency video coding (HEVC). In the conventional ALF algorithm, filter coefficients are re-designed for 16 times to obtain the best sets in one frame and also to increase the external memory access and computational cost significantly. This contribution proposes an inter-channel transformation scheme based on the correlation between chroma and luma to alleviate both computing time and memory bandwidth. First, the proposed algorithm generates the cross-correlation matrix and auto-correlation matrix in chroma samples which only spends one-quarter computational costs due to the resolution difference. Then, it applies the inter-channel transformation to derive the filter coefficients. The proposed methods not only eliminate a large number of multiplication operations, but also reduce the run-time by 58.70% averagely during whole ALF encoding process, while a negligible loss 0.074 BD-PSNR in the Y component.

Original languageEnglish
Title of host publicationProceedings of the IASTED International Conference on Signal and Image Processing, SIP 2012
Pages175-180
Number of pages6
DOIs
Publication statusPublished - 2012 Dec 1
Event14th IASTED International Conference on Signal and Image Processing, SIP 2012 - Honolulu, HI, United States
Duration: 2012 Aug 202012 Aug 22

Publication series

NameProceedings of the IASTED International Conference on Signal and Image Processing, SIP 2012

Conference

Conference14th IASTED International Conference on Signal and Image Processing, SIP 2012
Country/TerritoryUnited States
CityHonolulu, HI
Period12/8/2012/8/22

Keywords

  • ALF
  • Hevç
  • Inter-Channel Correlation

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing

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