A novel motion classification based intermode selection strategy for HEVC performance improvement

Pallab Kanti Podder, Manoranjan Paul, Manzur Murshed

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

High Efficiency Video Coding (HEVC) standard adopts several new approaches to achieve higher coding efficiency (approximately 50% bit-rate reduction) compared to its predecessor H.264/AVC with same perceptual image quality. Huge computational time has also increased due to the algorithmic complexity of HEVC compared to H.264/AVC. However, it is really a demanding task to reduce the encoding time while preserving the similar quality of the video sequences. In this paper, we propose a novel efficient intermode selection technique and incorporate into HEVC framework to predict motion estimation and motion compensation modes between current and reference blocks and perform faster inter mode selection based on three dissimilar motion types in divergent video sequences. Instead of exploring and traversing all the modes exhaustively, we merely select a subset of candidate modes and the final mode from the selected subset is determined based on their lowest Lagrangian cost function. The experimental results reveal that average encoding time can be downscaled by 40% with similar rate-distortion performance compared to the exhaustive mode selection strategy in HEVC.
Original languageEnglish
Pages (from-to)1211-1220
Number of pages10
JournalNeurocomputing
Volume173
Issue numberPart 3
Early online date2015
DOIs
Publication statusPublished - Jan 2016

Grant Number

  • DP130103670

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