Suzuki Taizo, Huang Liping
IEEE Trans Image Process. 2022;31:6072-6082. doi: 10.1109/TIP.2022.3205470. Epub 2022 Sep 22.
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.
使用光谱 - 空间变换的编解码器通过将彩色滤光片阵列（CFA采样的原始图像）捕获的原始相机图像的RGB颜色空间转换为去相关颜色空间，从而有效地压缩这些图像。本研究描述了两种类型的光谱 - 空间变换，称为扩展星 - 四面体变换（XSTT）及其边缘感知版本，称为边缘感知XSTT（EXSTT），它们无需额外比特（边信息）且额外复杂度很小。它们是通过以下方式获得的：（i）将作为最新光谱 - 空间变换之一的星 - 四面体变换（STT）扩展为我们先前提出的基于小波的光谱 - 空间变换的新版本和一个更简单的版本；（ii）考虑到小波变换的每个二维预测步骤是两个一维对角或水平 - 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能够很好地对CFA采样的原始图像进行去相关：对于高质量相机图像，它们将两个绿色分量之间的能量差异降低了约3.38 - 30.08%，对于手机图像降低了8.97 - 14.47%。基于JPEG 2000的CFA采样原始图像无损和有损压缩实验表明，其性能优于传统方法。对于高质量相机图像，XSTT/EXSTT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EXSTT在平均无损比特率方面将其提高了约0.03 - 0.19 bpp，XSTT在平均Bjøntegaard增量峰值信噪比方面将其提高了约0.16 - 0.96 dB。对于手机图像，我们之前的工作表现最佳，而XSTT/EXSTT呈现出与高质量相机图像情况相似的趋势。
