Lossless Compression of Color Filter Array Images by Hierarchical Prediction and Context Modeling

Abstract

This paper presents an encoder for the lossless compression of color filter array (CFA) data, which consists of a hierarchical predictor and context-adaptive arithmetic encoder. In hierarchical prediction, the subsampled images are encoded in order; each of the subimages contains only one color component (red, green, or blue) in the case of a Bayer CFA image. By subsampling, the green pixels are separated into two sets, one of which is encoded by a conventional grayscale encoder, and then is used to predict the green pixels in the other set. Both the sets of greens are then used to predict the reds, and the green and red pixels are used to predict the blues. Throughout this process, the predictors are designed considering the direction of the edges in the neighborhood. By gathering some information from the prediction process, such as edge activity and neighboring errors, the magnitude of prediction error is also estimated. From this, the probability distribution function of prediction error conditioned on neighboring pixels, i.e., the context is estimated, and context-adaptive arithmetic encoding is applied to reduce the resulting bits further. The experimental results on real and simulated CFA images show that the proposed method produces less bits per pixel than the conventional lossless image compression methods and recently developed lossless CFA compression algorithms.