在近年來，以Structure Similarity (SSIM) 為基礎做運算的Rate-Distortion Optimization (RDO) 已經廣被大家所研究並且發展得相當成熟，且比傳統以Mean Square Error (MSE) 為基礎的RDO更能符合人眼視覺上的評測。但由於以SSIM為基礎的RDO的計算複雜度比以MSE為基礎的RDO高出許多，在這裡我們使用計算比SSIM簡單又跟SSIM有良好相關性的Noise-to-Signal Ratio (NSR) 來近似SSIM。
首先為了證明NSR與SSIM有良好相關性，我們對NSR跟SSIM進行一系列相關性的分析，並且不同以往one-16x16-macroblock，我們提出four-8x8-macroblock的平均作為計算單位，會對這兩種做法做比較。
經由前面的實驗我們可以知道NSR與SSIM確實有良好的相關性，所以我們利用NSR作為distortion metric，並且發現在不同variance的區域可以使用不同QP來量化，所以提出利用變異數調整QP大小的Variance -Adaptive Quantization (VAQ) 做最佳化並在JM17.0上進行實驗，實驗結果顯示與JM17.0相比平均可以省下12%-23.3%的bitrate。

In recent years, rate-distortion optimization for structural similarity (SSIM) has been well developed and studied to improve visual quality for video coding. SSIM is also well matched to human visual system and has better perceptual quality than MSE. Because of complex computation of SSIM, we used Noise-to-Signal Ratio (NSR) to approximate SSIM.
First, in order to explain that NSR has good correlation with SSIM, we did some analysis on N SR and SSIM. What is different from previous work is that we proposed four-8x8-macroblock and nine-8x8-macroblock as our calculation unit instead of one-16x16-macroblock. Then we compared these three configurations and chose the better one: four-8x8-macroblock.
From the experiments above, we knew NSR has good correlation with SSIM indeed, so we used NSR as our distortion metric. We also found that regions with different variances can use different quantization steps to improve quality in terms of SSIM. Therefore, we proposed a variance-adaptive quantization algorithm which uses variance to scale quantization parameters. The experiment results using JM17.0 shows that we can save 12% - 23.3% bitrate compared with the original JM17.0.

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