Wavelet-Based SpectralÎÜSpatial Transforms for CFA- Sampled Raw Camera Image Compression


These spectral-spatial transforms (SSTs) are used to transform a raw camera image that was captured using a colour filter array (CFA-sampled image) from an RGB colour space made of red, green, and blue components into an uncorrelated colour space. The wavelet-based SSTs (WSSTs) described in this research are the result of rearranging all of the SSTs mentioned in this paper. There are three macropixel SSTs implemented in each 2x2 macropixel, and we'll explain how they work. Following that, we focus on two-channel Haar wavelet transforms and three-channel Haar-like wavelet transforms in each MSST and replace the Haar and Haar-like transforms with Cohen-Daubechies-Feauveau (CDF) 5/3 and 9/7 wavelet transforms, which are customised on the basis of the original pixel positions in 2D space. However, in lossless CFA sampling image compression using JPEG 2000, WSSTs improve the bitrates by about 1.67 percent to 3.17 percent when compared to the best SST, and the WSSTs that use 5/3 wavelet transform improves the bitrate by about 0.31% to 1.71 percentage points when compared to the best SST. WSSTs exhibit about 2.25-4.40 dB and 26.04-49.35 percent in the Bjntegaard measures (BD-PSNRs and BD-rates) compared to not utilising a transform, while WSSTs that use 9/7 wavelet transforms increase the metrics by about 0.013-0.40 dB and 2.27- 4.80 percent compared to the best available SSTs.

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