PREDICTION BASED LOSSLESS COLOR FILTER ARRAYCFA COMPRESSION
DESIGN DETAILS
In order to reduce cost, digital cameras use a single image sensor to capture color images. A Bayer color filter array (CFA) is usually coated over the sensor in these cameras to record only one of the three color components at each pixel location. In general, a CFA image is first interpolated via a demosaicing process to form a full color image before being compressed for storage. There are two categories of CFA image compression schemes: lossy and lossless. Lossy schemes compress a CFA image by discarding its visually redundant information. These schemes usually yield a higher compression ratio as compared with the lossless schemes. In some high-end photography applications such as commercial poster production, original CFA images are required for producing high quality full color images directly. In such cases, lossless compression of CFA images is necessary.
In this Matlab design a prediction-based lossless CFA compression scheme is developed. It divides a CFA image into two sub-images: a green sub-image which contains all green samples of the CFA image and a non-green sub-image which holds the red and the blue samples. The green sub-image is coded first, and the non-green sub-image follows based on the green sub-image as a reference. To reduce the spectral redundancy, the non-green sub-image is processed in the color difference domain whereas the green sub-image is processed in the intensity domain as a reference for the color difference content of the non-green sub-image. Both sub-images are processed in raster scan sequence with context matching based prediction technique to remove the spatial dependency. The prediction residue planes of the two sub-images are then entropy encoded sequentially with realization scheme of adaptive Rice code.
REFERENCES
Reference Paper-1: A Lossless Compression Scheme for Bayer Color Filter Array Images
Author’s Name: King-Hong Chung and Yuk-Hee Chan
Source: IEEE
Year: 2008
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