Lossless compression of Bayer mask images using an optimal vector prediction technique

In this paper a lossless compression technique for Bayer pattern images is presented. The common way to save these images was to colour reconstruct them and then code the full resolution images using one of the lossless or lossy methods. This solution is useful to show the captured images at once, but it is not convenient for efficient source coding. In fact, the resulting full colour image is three times greater than the Bayer pattern image and the compression algorithms are not able to remove the correlations introduced by the reconstruction algorithm. However, the Bayer pattern images present new problems for the coding step. In fact, adjacent pixels belong to different colour bands mixing up different kinds of correlations. In this paper we present a lossless compression procedure based on an optimal vector predictor, where the Bayer pattern is divided into non-overlapped 2×2 blocks, each of them predicted as a vector. We show that this solution is able to exploit the existing correlation giving a good improvement of the compression ratio with respect to other lossless compression techniques, e.g., JPEG-LS.