A lossless image coder with context classification, adaptive prediction and adaptive entropy coding

Abstract

In this paper, we combine a context classification scheme with adaptive prediction and entropy coding to produce an adaptive lossless image coder. In this coder, we maximize the benefits of adaptivity using both adaptive prediction and entropy coding. The adaptive prediction is closely tied with the classification of contexts within the image. These contexts are defined with respect to the local edge, texture or gradient characteristics as well as local activity within small blocks of the image. For each context an optimal predictor is found which is used for the prediction of all pixels belonging to that particular context. Once the predicted values have been removed from the original image, a clustering algorithm is used to design a separate, optimal entropy coding scheme for encoding the prediction residual. Blocks of residual pixels are classified into a finite number of classes and members of each class are encoded using the entropy coder designed for that particular class. The combination of these two powerful techniques produces some of the best lossless coding results reported so far.

Extended Multi WLS Method for Lossless Image Coding

“…Quantization levels were 3 experimentally found thresholds for : 300, 2000, 8000. A similar idea was presented in [ 40 ].…”

“…They are defined by m G values m = {1, 1, 2, 3, 4, 12}. The current p parameter is calculated as p = (K − 1)/K, where K = ω 2 for m = 48 (40). Then, m G is evaluated [49,50]:…”

Efficient adaptive arithmetic coding based on updated probability distribution for lossless image compression

“…Recently, many lossless schemes for image compression [1][2][3][4] based on arithmetic coding ͑AC͒ 5,6 have been proposed. The goal of these schemes is to represent an image with the smallest possible number of bits without loss of any information in order to speed up transmission and minimize storage requirements.…”