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Improving the intra-prediction of H.264 and H.265 video coding standards using adaptive weighted least squares based predictor
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Publication Year
2018-01-01
Journal
Lecture Notes in Electrical Engineering
Publisher
Springer Verlag
Citation
Lecture Notes in Electrical Engineering, Vol.425, pp.176-186
Keyword
Contextual pattern matchingH.264/AVCH.265/HEVCIntra-predictionLeast squares based prediction
Mesh Keyword
H.264/AVCH.265/HEVCHigh-efficiency video codingIntra PredictionLeast SquareOrdinary least squaresVideo coding standardWeighted least squares
All Science Classification Codes (ASJC)
Industrial and Manufacturing Engineering
Abstract
Advanced Video Coding (H.264/AVC) and its recent extension High Efficiency Video Coding (H.265/HEVC) are the current industrial video coding standards that have directional and planar intra-predictors. The intra-prediction scheme of both standards is dominated by directional predictors. Ordinary least squares (OLS) based adaptive predictor is superior around edge pixels without considering the direction of an edge explicitly. In OLS based predictor, each pixel in the local area contributes equal weight to estimate prediction parameters. We observed that this does not hold true due to noise and the existence of different correlations between the causal context of each neighboring pixel. To address this problem, we proposed an adaptive weighted least squares (AWLS) based predictor that assigns different weights to neighboring pixels to reflect its relative contribution. Experimental results show that the proposed method outperforms the OLS based intraprediction for directional images and marginal improvements are obtained for other tested images and videos.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36342
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85022216136&origin=inward
DOI
https://doi.org/2-s2.0-85022216136
Journal URL
http://www.springer.com/series/7818
Type
Conference
Funding
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education [2016R1D1A1B03933875].
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Sohn, Kyung-Ah Image
Sohn, Kyung-Ah손경아
Department of Software and Computer Engineering
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