Abstract:
Spectral-spatial transforms (SSTs) convert 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, with luma, difference green, and two chroma components.
Reorganizing all SSTs in this paper yields three wavelet-based SST (WSST) types. First, each 2 _ 2 macropixel has three types of macropixel SST (MSST). Next, we replace the Haar and Haar-like wavelet transforms in each MSST with Cohen-Daubechies-Feauveau (CDF) 5/3 and 9/7 wavelets, which are customized based on the original pixel positions in 2D space.
In lossless CFA-sampled image compression based on JPEG 2000, WSSTs improve bitrates by 1.67%-3.17% compared to not using a transform and by 0.31%-0.71% compared to the best SST. In lossy CFA-sampled image compression based on JPEG 2000, WSSTs improve Bjåntegaard metrics (BD-PSNRs and BD-rates) by 2.25-4.40 dB and 26.04%-49.35% compared to not using a transform, and WSSTs that use 9/7 wavelet transforms improve metrics by 0.13-0.40 dB and 2.27%-4.80% compared to the best SST.
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