Codecs using spectral-spatial transforms efficiently compress raw camera images captured with a color filter array (CFA-sampled raw images) by changing their RGB color space into a decorrelated color space. This study describes two types of spectral-spatial transform, called extended Star-Tetrix transforms (XSTTs), and their edge-aware versions, called edge-aware XSTTs (EXSTTs), with no extra bits (side information) and little extra complexity. They are obtained by (i) extending the Star-Tetrix transform (STT), which is one of the latest spectral-spatial transforms, to a new version of our previously proposed wavelet-based spectral-spatial transform and a simpler version; (ii) considering that each 2D predict step of the wavelet transform is a combination of two 1D diagonal or horizontal-vertical transforms; (iii) weighting the transforms along the edge directions in the images. Compared with XSTTs, the EXSTTs can decorrelate CFA-sampled raw images well: they reduce the difference in energy between the two green components by about 3.38–30.08 % for high-quality camera images and 8.97–14.47 % for mobile phone images. The experiments on JPEG 2000-based lossless and lossy compression of CFA-sampled raw images show better performance than conventional methods. For high-quality camera images, the XSTTs/EXSTTs produce results equal to or better than the conventional methods: especially for images with many edges, the type-I EXSTT improves them by about 0.03–0.19 bpp in average lossless bitrate and the XSTTs improve them by about 0.16–0.96 dB in average Bjøntegaard delta peak signal-to-noise ratio. For mobile phone images, our previous work perform the best, whereas the XSTTs/EXSTTs show similar trends to the case of high-quality camera images.

中文翻译：

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用于 CFA 采样原始相机图像压缩的边缘感知扩展 Star-Tetrix 变换

使用光谱空间变换的编解码器通过将 RGB 颜色空间更改为去相关颜色空间，有效地压缩使用滤色器阵列捕获的原始相机图像（CFA 采样的原始图像）。这项研究描述了两种类型的光谱空间变换，称为扩展 Star-Tetrix 变换 (XSTT)，以及它们的边缘感知版本，称为边缘感知 XSTT (EXSTT)，没有额外的位（边信息）和额外的复杂性。它们是通过 (i) 将作为最新的光谱空间变换之一的 Star-Tetrix 变换 (STT) 扩展到我们先前提出的基于小波的光谱空间变换的新版本和更简单的版本；(ii) 考虑到小波变换的每个 2D 预测步骤是两个 1D 对角线或水平-垂直变换的组合；(iii) 对图像中沿边缘方向的变换进行加权。与 XSTT 相比，EXSTT 可以很好地对 CFA 采样的原始图像进行去相关：它们将高质量相机图像的两个绿色分量之间的能量差异减少了约 3.38-30.08%，手机图像减少了 8.97-14.47%。基于 JPEG 2000 的 CFA 采样原始图像的无损和有损压缩实验显示出比传统方法更好的性能。对于高质量的相机图像，XSTTs/EXSTTs 产生的结果等于或优于传统方法：特别是对于具有许多边缘的图像，I 型 EXSTTs 的平均无损比特率提高了约 0.03-0.19 bpp，XSTTs 提高了它们的平均 Bjøntegaard delta 峰值信噪比约为 0.16–0.96 dB。对于手机图像，我们之前的工作表现最好，