Sub-Sampled Cross-Component Prediction for Emerging Video Coding Standards

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

View graph of relations

Author(s)

  • Junru Li
  • Li Zhang
  • Kai Zhang
  • Shanshe Wang
  • Siwei Ma
  • Wen Gao

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)7305-7316
Journal / PublicationIEEE Transactions on Image Processing
Volume30
Online published17 Aug 2021
Publication statusPublished - 2021

Abstract

Cross-component linear model (CCLM) prediction has been repeatedly proven to be effective in reducing the inter-channel redundancies in video compression. Essentially speaking, the linear model is identically trained by employing accessible luma and chroma reference samples at both encoder and decoder, elevating the level of operational complexity due to the least square regression or max-min based model parameter derivation. In this paper, we investigate the capability of the linear model in the context of sub-sampled based cross-component correlation mining, as a means of significantly releasing the operation burden and facilitating the hardware and software design for both encoder and decoder. In particular, the sub-sampling ratios and positions are elaborately designed by exploiting the spatial correlation and the inter-channel correlation. Extensive experiments verify that the proposed method is characterized by its simplicity in operation and robustness in terms of rate-distortion performance, leading to the adoption by Versatile Video Coding (VVC) standard and the third generation of Audio Video Coding Standard (AVS3).

Research Area(s)

  • AVS3, Cross-component linear model, cross-component prediction, video coding, VVC

Citation Format(s)

Sub-Sampled Cross-Component Prediction for Emerging Video Coding Standards. / Li, Junru; Wang, Meng; Zhang, Li; Wang, Shiqi; Zhang, Kai; Wang, Shanshe; Ma, Siwei; Gao, Wen.

In: IEEE Transactions on Image Processing, Vol. 30, 2021, p. 7305-7316.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review