A VECTOR QUANTIZATION BASED APPROACH FOR CFA DATA COMPRESSION IN WIRELESS ENDOSCOPY CAPSULE

A novel approach for near-lossless compression of Color Filtering Array （CFA） data in wireless endoscopy capsule is proposed in this paper. The compression method is based on pre-processing and vector quantization. First, the CFA raw data are low pass filtered and rearranged during pre-processing. Then, pairs of pixels are vector quantized into macros of 9 bits by applying block partition and index mapping in succession. These macros are entropy compressed by Joint Photographic Experts Group-Lossless Standard （JPEG-LS） finally. The complex step of codeword searching in Vector Quantization （VQ） is avoided by a predefined partition rule, which is suitable for hardware implementation. By control of the pre-processor and VQ scheme, either high quality compression under unfiltered case or high ratio compression under filtered case can be realized, with the average Peak Signal-to-Noise Ratio （PSNR） more than 43dB and 37dB respectively. Compared with the state-of-the-art method and the previously proposed method, our compression approach outperforms in compression performance as well as in flexibility.