论文标题
边缘感知的扩展星形四分转换，用于CFA采样的原始相机图像压缩
Edge-Aware Extended Star-Tetrix Transforms for CFA-Sampled Raw Camera Image Compression
论文作者
论文摘要
使用光谱空间变换的编解码器有效地压缩了使用颜色滤镜阵列捕获的原始摄像机图像（CFA采样的原始图像），通过将其RGB颜色空间更改为反相关的颜色空间。这项研究描述了两种类型的频谱空间变换，称为扩展的星形 - 四十条变换（XSTTS）及其边缘感知版本，称为边缘感知的XSTTS（EXSTTS），没有额外的位（侧面信息）和少量的额外复杂性。 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 2-D predict step of the wavelet transform is a combination of two 1-D diagonal or horizontal-vertical transforms, and (iii) weighting the transforms along the edge directions in the images.与XSTT相比，EXSTT可以很好地解除CFA采样的原始图像：它们将两个绿色组件之间的能量差异降低了3.38美元左右 - 高质量相机图像的$ 30.08 $ \％\％\％$ \％，$ 8.97 $ - $ 14.47 $ $ 14.47 $ \ f for Moceal Phone图像的％$ \％。基于JPEG 2000的无损和损耗压缩的实验比常规方法显示出更好的性能。 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.对于手机图像，我们以前的工作表现最好，而XSTTS/EXSTT显示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 2-D predict step of the wavelet transform is a combination of two 1-D diagonal or horizontal-vertical transforms, and (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.
