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. [ABSTRACT FROM AUTHOR]