Mode-Dependent Distortion Modeling for H.264/SVC Coarse Grain SNR Scalability
碩士 === 國立交通大學 === 資訊科學與工程研究所 === 103 === In real-time video encoding applications, rate and distortion models play an important role in speeding up parameter decision. Most of the rate and distortion models are built for the single-layer video coding in the frame level. Only few models were develope...
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| Format: | Others |
| Language: | en_US |
| Published: |
2015
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| Online Access: | http://ndltd.ncl.edu.tw/handle/64150456982042881831 |
| Summary: | 碩士 === 國立交通大學 === 資訊科學與工程研究所 === 103 === In real-time video encoding applications, rate and distortion models play an important role in speeding up parameter decision. Most of the rate and distortion models are built for the single-layer video coding in the frame level. Only few models were developed to estimate the rate and distortion for multiple-layer video coding, not to mention models that are able to do mode-dependent estimation in the MB level. This thesis presents a mode-dependent distortion model for H.264/SVC coarse grain SNR scalability. It estimates the base-layer and enhancement-layer’s distortions with particular consideration of their prediction modes and inter-layer residual prediction. Based on a parametric signal model, the variances of the transformed prediction residual at both layers are first formulated analytically and approximated empirically. The results are then incorporated into the assumption that the transform coefficients are distributed according to the Laplacian distribution to obtain the final distortion estimates. Experimental results confirm its fairly good ability to predict the actual distortions in both the frame and macroblock levels.
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