A STUDY OF PREDICTION METHODS BASED ON MACHINE LEARNING TECHNIQUES FOR LOSSLESS IMAGE CODING

In recent years, a new research strategy for coding has emerged by exploring the advances brought by modern machine learning techniques. Novel hybrid coding solutions were proposed by replacing specific modules in conventional coding frameworks with more efficient modules based on innovative deep-learning (DL) methods. The paper studies first our recently proposed DL-based prediction methods for lossless image coding by analyzing their designs and employed ML concepts. A novel neural network architecture is proposed, based on a new structure of layers which proves to deliver an improved prediction compared to the reference designs. Context-tree based bit-plane coding is employed to encode the resulting prediction error. The experimental results reveal that the proposed codec reduces the lossless coding rate with 1.9% compared to state-of-the-art DL-based methods while having 4.95% less parameters. The performance gap of almost 50% compared to traditional codecs recommends the use of ML-based tools in the design of future standards for lossless image compression.