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PROJECT TITLE :

Hierarchical Prediction and Context Adaptive Coding for Lossless Color Image Compression - 2014

ABSTRACT:

This paper presents a brand new lossless color image compression algorithm, based mostly on the hierarchical prediction and context-adaptive arithmetic coding. For the lossless compression of an RGB image, it's initial decorrelated by a reversible color transform and then Y component is encoded by a standard lossless grayscale image compression methodology. For encoding the chrominance images, we have a tendency to develop a hierarchical theme that permits the employment of upper, left, and lower pixels for the pixel prediction, whereas the conventional raster scan prediction strategies use higher and left pixels. An acceptable context model for the prediction error is also outlined and also the arithmetic coding is applied to the error signal corresponding to every context. For several sets of pictures, it is shown that the proposed methodology any reduces the bit rates compared with JPEG200zero and JPEG-XR.


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Arithmetic Codes Data Compression Prediction Theory Inverse Transforms Adaptive Codes Decorrelation Image Coding Image Colour Analysis Context Adaptive Arithmetic Coding Lossless Color Image Compression Reversible Color Transform Hierarchical Prediction
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