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- W1569215377 abstract "Typically the same transform, the 2-D DiscreteCosine Transform (DCT), is used to compress both image intensitiesin image coding and prediction residuals in video coding. Majorprediction residuals include the motion compensated predictionresidual, the resolution enhancement residual in scalable videocoding, and the intra prediction residual in intra-frame coding.The 2-D DCT is efficient at decorrelating images, but the spatialcharacteristics of prediction residuals can be significantlydifferent from the spatial characteristics of images, anddeveloping transforms that are adapted to the characteristics ofprediction residuals can improve their compression efficiency. Inthis thesis, we explore the differences between the characteristicsof images and prediction residuals by analyzing their localanisotropic characteristics and develop transforms adapted to thelocal anisotropic characteristics of some types of predictionresiduals. The analysis shows that local regions in images have 2-Danisotropic characteristics and many regions in several types ofprediction residuals have 1-D anisotropic characteristics. Based onthis insight, we develop 1-D transforms for these residuals. Weperform experiments to evaluate the potential gains achievable fromusing these transforms within the H.264 codec, and the experimentalresults indicate that these transforms can increase the compressionefficiency of these residuals." @default.
- W1569215377 created "2016-06-24" @default.
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- W1569215377 date "2010-01-01" @default.
- W1569215377 modified "2023-09-26" @default.
- W1569215377 title "Transforms for prediction residuals in video coding" @default.
- W1569215377 hasPublicationYear "2010" @default.
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