A Lossless Compression Scheme for Bayer Color Filter Array Images

Abstract

In most digital cameras, Bayer color filter array (CFA) images are captured and demosaicing is generally carried out before compression. Recently, it was found that compression-first schemes outperform the conventional demosaicing-first schemes in terms of output image quality. An efficient prediction-based lossless compression scheme for Bayer CFA images is proposed in this paper. It exploits a context matching technique to rank the neighboring pixels when predicting a pixel, an adaptive color difference estimation scheme to remove the color spectral redundancy when handling red and blue samples, and an adaptive codeword generation technique to adjust the divisor of Rice code for encoding the prediction residues. Simulation results show that the proposed compression scheme can achieve a better compression performance than conventional lossless CFA image coding schemes.

On the Utilization of Reversible Colour Transforms for Lossless 2-D Data Compression

“…Initially, the images are de-interlaced into individual channels followed by the weighted template matching based prediction is performed compressing the high-resolution images at a very low computational cost. Chung et al in [51] proposed a method to use context matching accompanied with reduction techniques in the spectral domain for colour difference estimation.…”

“…Bits Per Pixel (BPPs) achieved by the schemes in the discussion are represented in Figure 9. JPEG-LS, JPEG2000, LCMI, CMBP and HPCM techniques [51] are used as a benchmark scheme to draw a comparison with the proposed system. YUV RCT based BBWCA of Khan et al in [14] is also compared with the proposed method to determine its compression efficiency and performance.…”