Wavelet-Based Spectral-Spatial Transforms for CFA-Sampled Raw Camera Image Compression

Spectral-spatial transforms (SSTs) change a raw camera image captured using a color filter array (CFA-sampled image) from an RGB color space composed of red, green, and blue components into a decorrelated color space, such as YDgCbCr or YDgCoCg color space composed of luma, difference green, and two chroma components. This paper describes three types of wavelet-based SST (WSST) obtained by reorganizing all of the existing SSTs covered in this paper. First, we introduce three types of macropixel SST (MSST) implemented within each 2 \times 2 macropixel. Next, we focus on two-channel Haar wavelet transforms, which are simple wavelet transforms, and three-channel Haar-like wavelet transforms in each MSST and replace the Haar and Haar-like wavelet transforms with Cohen-Daubechies-Feauveau (CDF) 5/3 and 9/7 wavelet transforms, which are customized on the basis of the original pixel positions in 2D space. Although the test data set is not big, in lossless CFA-sampled image compression based on JPEG 2000, the WSSTs improve the bitrates by about 1.67%-3.17% compared with not using a transform, and the WSSTs that use 5/3 wavelet transforms improve the bitrates by about 0.31%-0.71% compared with the best existing SST. Moreover, in lossy CFA-sampled image compression based on JPEG 2000, the WSSTs show about 2.25-4.40 dB and 26.04%-49.35% in the Bjøntegaard metrics (BD-PSNRs and BD-rates) compared with not using a transform, and the WSSTs that use 9/7 wavelet transforms improve the metrics by about 0.13-0.40 dB and 2.27%-4.80% compared with the best existing SST.