Lossless and Near-Lossless Image Compression Using Generalized Multi-Context Linear and Nonlinear Prediction
TL;DR
The paper proposes the Multi-ctx2 method, which enables image compression in lossless and near-lossless modes by employing a generalized multi-context division method in the prediction stage, which is more efficient than other fast prediction methods.
Abstract
The paper proposes the Multi-ctx2 method, which enables image compression in lossless and near-lossless modes. It employs a generalized multi-context division method in the prediction stage, which is more efficient than other fast prediction methods. Five simple rules for computing the context number have been developed, which serve not only to identify an individualized linear predictor but also to correct the cumulative prediction error. In subsequent stages of the encoder, prediction errors are encoded in a two-stage process: first using an adaptive Golomb code, then a binary adaptive arithmetic encoder. The proposed method is characterized by short compression and decompression times while offering a good compromise between compression efficiency and encoding/decoding time. The proposed prediction method can be easily implemented in hardware due to its use of fixed-point arithmetic. Unlike many other solutions, there is no need to access the entire image data during encoding to tune the encoder parameters to a specific image. The paper demonstrates the efficiency of the proposed solution compared to competing solutions, showing improvements of 6.85% and 7.03% over JPEG-LS in lossless mode (and 10.2% in near-lossless mode) across two test sets.