DCT quantization matrices visually optimized for individual images
DCT quantization matrices visually optimized for individual images
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DOI：
10.1117/12.152694
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发表时间：
1993-09
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影响因子：
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通讯作者：
Andrew B. Watson
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文献类型：
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作者：
Andrew B. Watson
多种图像压缩标准（JPEG、MPEG、H.261）均基于离散余弦变换 (DCT)。这些标准没有指定实际的 DCT 量化矩阵。 Ahumada & Peterson 和 Peterson、Ahumada & Watson 提供了计算感知无损量化矩阵的数学公式。在这里，我展示了如何计算针对特定图像优化的矩阵。该方法将每个 DCT 系数视为视觉“通道”局部响应的近似值。对于给定的量化矩阵，DCT 量化误差通过对比敏感度、光适应和对比掩蔽进行调整，并在图像块上非线性汇集。这会产生一个 8 X 8 的“感知误差矩阵”。对感知误差矩阵的第二个非线性池化产生总感知误差。通过该模型，我们可以估计特定图像的量化矩阵，该矩阵对于给定的总感知误差产生最小比特率，或者对于给定的比特率产生最小感知误差。许多图像的自定义矩阵比图像无关矩阵有明显的改进。自定义矩阵与 JPEG 标准兼容，该标准需要传输量化矩阵。
Several image compression standards (JPEG, MPEG, H.261) are based on the Discrete Cosine Transform (DCT). These standards do not specify the actual DCT quantization matrix. Ahumada & Peterson and Peterson, Ahumada & Watson provide mathematical formulae to compute a perceptually lossless quantization matrix. Here I show how to compute a matrix that is optimized for a particular image. The method treats each DCT coefficient as an approximation to the local response of a visual `channel.' For a given quantization matrix, the DCT quantization errors are adjusted by contrast sensitivity, light adaptation, and contrast masking, and are pooled non-linearly over the blocks of the image. This yields an 8 X 8 `perceptual error matrix.' A second non-linear pooling over the perceptual error matrix yields total perceptual error. With this model we may estimate the quantization matrix for a particular image that yields minimum bit rate for a given total perceptual error, or minimum perceptual error for a given bit rate. Custom matrices for a number of images show clear improvement over image-independent matrices. Custom matrices are compatible with the JPEG standard, which requires transmission of the quantization matrix.