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  • 學位論文

使用AVC及HEVC的編碼決策之無參考畫面影像品質度量法

No-Reference Video Quality Metrics Using Encoding Decisions in AVC and HEVC Coded Videos

指導教授 : 林鼎然
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摘要


在本論文中,我們提出了在AVC (Advanced Video Coding, H.264)以及HEVC (High Efficiency Video Coding)編碼影像中。『無參考影像壓縮品質的模型』來預測『完全參考的PSNR以及SSIM度量法』。我們使用的模型為支持向量回歸(support vector regression SVR),可將特徵轉換到更高維度的空間來實現更佳的預估表現,我們只有使用當移動補償中的編碼決策來實現這個預測,而不需要來自於像素域的訊息,因此這非常的有效率。我們可以看出,SVR模型使用與區塊分割相關的因子,在AVC的影像預測中表現良好(在PSNR部分有0.81的相關性,SSIM有0.79)。這個方法,對於HEVC的影像提供了更高的預測表現(PSNR部分有0.91,SSIM則有0.89),原因來自於HEVC比起AVC有更為複雜的區塊分割。在PSNR的預測中,在HEVC模型的表現中比起AVC的模型好上12%,而在SSIM的預測為11%。其他的模型表現評估RMSE以及R^2,也支持這個結果,在本文中,比起其他增加影像編碼效率的文章,在HEVC中複雜的編碼參數,對比AVC來說,HEVC可以提供更多的訊息來得到原始畫面的樣貌。

並列摘要


In this paper, we propose no-reference compressed video quality models to predict the full-reference PSNR and SSIM metrics for AVC (Advanced Video Coding, H.264) and HEVC (High Efficiency Video Coding) encoded videos. The model we used is support vector regression (SVR) that transforms the features into higher-dimensional space to achieve better prediction performance. We use only encoding decisions made during motion estimation to perform the prediction, and do not need the information from pixel domain, so it is very efficient. We show that the SVR model containing the factors related to the statistics about block partitions in a frame can predict the video quality well for AVC videos (0.81 correlation for PSNR, and 0.79 for SSIM). This approach provides even higher prediction performance for HEVC videos (0.91 for PSNR and 0.89 for SSIM) due to its more complex partition decisions than AVC; the improvement of HEVC model over AVC model is 12% for PSNR prediction, and 11% for SSIM prediction. Other model performance measurement RMSE and R2 are also provided to support the results. This paper demonstrates that, other than increasing the encoding efficiency, the complex encoding parameters in HEVC can provide more information about original frames compared to AVC.

參考文獻


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