H.264 video coding with multiple weighted prediction models

Sik Ho Tsang; Yui Lam Chan

doi:10.1109/ICIP.2010.5654032

H.264 video coding with multiple weighted prediction models

The Hong Kong Polytechnic University

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

6 Citations (Scopus)

Abstract

Weighted prediction is a video coding tool to encode scenes with brightness variations. However, no single WP model works well for all types of brightness variations. In this paper, a novel single reference frame multiple WP models (SRefMWP) scheme is proposed to facilitate the use of multiple WP models in different macroblocks of the current frame even when they are predicted from the same reference. It provides this feature by making a new arrangement of the multiple frame buffers in multiple reference frame motion estimation. Experimental results show that the proposed SRefMWP can improve prediction in scenes with different types of brightness variations, and even benefit to scenes that contain local brightness variation.

Original language	English
Title of host publication	2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings
Pages	2069-2072
Number of pages	4
DOIs	https://doi.org/10.1109/ICIP.2010.5654032
Publication status	Published - 1 Dec 2010
Event	2010 17th IEEE International Conference on Image Processing, ICIP 2010 - Hong Kong, Hong Kong Duration: 26 Sept 2010 → 29 Sept 2010

Conference

Conference	2010 17th IEEE International Conference on Image Processing, ICIP 2010
Country/Territory	Hong Kong
City	Hong Kong
Period	26/09/10 → 29/09/10

Keywords

Brightness variations
H.264
Multiple reference frames
Weighted prediction

ASJC Scopus subject areas

Software
Computer Vision and Pattern Recognition
Signal Processing

More information

10.1109/ICIP.2010.5654032

Cite this

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title = "H.264 video coding with multiple weighted prediction models",

abstract = "Weighted prediction is a video coding tool to encode scenes with brightness variations. However, no single WP model works well for all types of brightness variations. In this paper, a novel single reference frame multiple WP models (SRefMWP) scheme is proposed to facilitate the use of multiple WP models in different macroblocks of the current frame even when they are predicted from the same reference. It provides this feature by making a new arrangement of the multiple frame buffers in multiple reference frame motion estimation. Experimental results show that the proposed SRefMWP can improve prediction in scenes with different types of brightness variations, and even benefit to scenes that contain local brightness variation.",

keywords = "Brightness variations, H.264, Multiple reference frames, Weighted prediction",

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AB - Weighted prediction is a video coding tool to encode scenes with brightness variations. However, no single WP model works well for all types of brightness variations. In this paper, a novel single reference frame multiple WP models (SRefMWP) scheme is proposed to facilitate the use of multiple WP models in different macroblocks of the current frame even when they are predicted from the same reference. It provides this feature by making a new arrangement of the multiple frame buffers in multiple reference frame motion estimation. Experimental results show that the proposed SRefMWP can improve prediction in scenes with different types of brightness variations, and even benefit to scenes that contain local brightness variation.

KW - Brightness variations

KW - H.264

KW - Multiple reference frames

KW - Weighted prediction

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U2 - 10.1109/ICIP.2010.5654032

DO - 10.1109/ICIP.2010.5654032

M3 - Conference article published in proceeding or book

SN - 9781424479948

SP - 2069

EP - 2072

BT - 2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings

T2 - 2010 17th IEEE International Conference on Image Processing, ICIP 2010

Y2 - 26 September 2010 through 29 September 2010

ER -

H.264 video coding with multiple weighted prediction models

Abstract

Conference

Keywords

ASJC Scopus subject areas

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