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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/48319


    Title: 基於量化失真模型下H.264/SVC空間可調性視訊編碼之品質估測;Quality Estimation for H.264/SVC Spatial Scalability based on a New Quantization Distortion Model
    Authors: 江彥廷;Yan-ting Jiang
    Contributors: 通訊工程研究所
    Keywords: 量化失真模型;品質估測;可調式視訊編碼;Quantization-Distortion Model;H.264;Scalable Video Coding;Spatial Scalability;Quality Estimation
    Date: 2011-07-25
    Issue Date: 2012-01-05 14:51:08 (UTC+8)
    Abstract: H.264/SVC可調式視訊編碼(Scalable Video Coding, SVC)是現在最新的可調式視訊編碼標準,它提供了時間可調性(Temporal Scalability)、空間可調性(Spatial Scalability)及品質可調性(Qualitys Scalability)三大工具,且均由一個基礎層(Base Layer)及數個增強層(Enhancement Layer)所構成,其中Base Layer的編碼方式可相容於H.264/AVC,而Enhancement Layer除了可自行做預測與編碼外,亦利用Base Layer之編碼資訊進行預測與編碼,所以其壓縮效率可以比之前的可調式視訊編碼標準來的高。而不同的使用情境需要不同的視訊品質,如裝置解析度大小不同、網路傳輸頻寬有限等,因此如何有效率的提供合適的視訊品質給各種不同使用情境的使用者是一個很重要的議題。 本論文利用層際間預測的概念,經理論分析所得壓縮前預測之殘餘值(Prior-Residual)以及量化參數來建立用於H.264/SVC可調式視訊編碼的量化失真模型,經實驗結果發現,利用提出的失真模型所預估出的編碼失真和實際經過壓縮後所得之編碼失真相比,精確率最高可達到94.98%。 Scalable Video Coding (SVC) provides efficient compression for the video bitstream equipped with various scalable configurations. H.264 scalable extension (H.264/SVC) is the most recent scalable coding standard. It involves state-of-the-art inter-layer prediction to provide higher coding efficiency than previous standards. Moreover, the requirements for the video quality on distinct situations like link conditions or video contents are usually different. Therefore, how to efficiently provide suitable video quality to users under different situations is an important issue. This work proposes a Quantization-Distortion (Q-D) model for H.264/SVC spatial scalability to estimate video quality before real encoding is performed. We introduce the residual decomposition for three inter-layer prediction types: residual prediction, intra prediction, and motion prediction. The residual can be decomposed to previous distortion and prior-residual that can be estimated before encoding. For single layer, they are distortion of previous frame and difference between two original frames. Then, the distortion can be modeled as a function of quantization step and prior-residual. In simulations, the proposed model can estimate the actual Q-D curves for each inter-layer prediction, and the accuracy of the model is up to 94.98%.
    Appears in Collections:[通訊工程研究所] 博碩士論文

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