Edge-aware extended Star-Tetrix transforms for CFA-sampled raw camera image compression
Edge-aware extended Star-Tetrix transforms for CFA-sampled raw camera image compression
复制标题
用于 CFA 采样原始相机图像压缩的边缘感知扩展 Star-Tetrix 变换
DOI：
10.1109/tip.2022.3205470
复制
发表时间：
2022
影响因子：
10.6
通讯作者：
Taizo Suzuki and Liping Huang
中科院分区：
文献类型：
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作者：
D. Kawaguchi;S. Tomita;S. Hirayama;Y. Inui;冨田壮平，川口大貴，平山智士，乾 義尚;冨田壮平，川口大貴，平山智士，乾 義尚;Taizo Suzuki and Liping Huang
使用光谱-空间变换的编解码器通过将原始相机图像的RGB颜色空间更改为去相关颜色空间，从而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，称为扩展的星形-TETRIX变换(XSTT)，以及它们的边缘感知版本，称为边缘感知XSTT(EXSTT)，没有额外的比特(边信息)，并且几乎没有额外的复杂度。它们是通过(I)将最新的谱-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预测步骤是两个一维对角或水平-垂直变换的组合；(Iii)沿图像的边缘方向对变换进行加权。与XSTT相比，EXSTT能够很好地去相关CFA采样的原始图像：对于高质量的相机图像，它们将两个绿色分量之间的能量差减少约3.38~30.08%；对于手机图像，它们分别减少约8.97~14.47%。对CFA采样的原始图像进行了基于JPEG2000的无损压缩和有损压缩实验，结果表明该方法比传统方法具有更好的压缩性能。对于高质量的相机图像，XSTT/EXSTT产生与传统方法相同或更好的结果：尤其是对于具有多个边缘的图像，类型I EXSTT在平均无损比特率方面将它们提高了约0.03-0.19bpp，在平均Bjntegaard Delta峰值信噪比上提高了约0.16-0.96db。对于手机图像，我们之前的工作表现最好，而XSTT/EXSTT表现出与高质量相机图像相似的趋势。
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.