Approach For Lossless Image Compression Research Articles

An Integrated Approach for Lossless Image Compression Using CLAHE, Two-Channel Encoding and Adaptive Arithmetic Coding

An Integrated Approach for Lossless Image Compression Using CLAHE, Two-Channel Encoding and Adaptive Arithmetic Coding

Deep-Learning based Lossless Image Coding

This paper proposes a novel approach for lossless image compression. The proposed coding approach employs a deep-learning-based method to compute the prediction for each pixel, and a context-tree-based bit-plane codec to encode the prediction errors. First, a novel deep learning-based predictor is proposed to estimate the residuals produced by traditional prediction methods. It is shown that the use of a deep-learning paradigm substantially boosts the prediction accuracy compared with the traditional prediction methods. Second, the prediction error is modeled by a context modeling method and encoded using a novel context-tree-based bit-plane codec. Codec profiles performing either one or two coding passes are proposed, trading off complexity for compression performance. The experimental evaluation is carried out on three different types of data: photographic images, lenslet images, and video sequences. The experimental results show that the proposed lossless coding approach systematically and substantially outperforms the state-of-the-art methods for each type of data.

Hybridized Approach for Lossless Image Compression

Hybridized Approach for Lossless Image Compression

Combinatorial transforms: Applications in lossless image compression

Common image compression standards are usually based on frequency transform such as Discrete Cosine Transform. We present a different approach for lossless image compression, which is based on a combinatorial transform. The main transform is Burrows Wheeler Transform (BWT) which tends to reorder symbols according to their following context. It becomes one of promising compression approach based on context modeling. BWT was initially applied for text compression software such as BZIP2; nevertheless it has been recently applied to the image compression field. Compression schemes based on the Burrows Wheeler Transform have been usually lossless; therefore we implement this algorithm in medical imaging in order to reconstruct every bit. Many variants of the three stages which form the original compression scheme can be found in the literature. We propose an analysis of the latest methods and the impact of their association and present an alternative compression scheme with a significant improvement over the current standards such as JPEG and JPEG2000.