Image cellular automata coding based on resolution progressive experiments with color spaces and file formats

Scalability or Progressive is a method used to reduce the quality used in Image Compression. There are several types of scalability: Quality, Resolution and Component. Each type is considered and calculated differently depending on purpose. In this research, we consider aspects of Resolution Progressive. Currently, the methods commonly used for this image encoding include Wavelet and Laplacian Pyramids. The objective is to maximize the quality of information that is encoded and to display the results in multiple resolutions. This paper presents an encoding method consisting of Reversible Cellular Automata and Intra-prediction. Reversible Cellular Automata methods are used for data conversion to adjust the resolution and Intra-prediction is applied to reduce redundant data that is to be compressed. Necessary experiments with color images are used to test the stability of the data after saving various image formats such as BMP and JPEG using the PSNR and SSIM performance measures. In this regard, the proposed method experiments with RGB color and BMP formats which provide more results than the other color space and file formats.