Testing of Image Compression Methods

Seven console codecs BMF 2.01 (BMF.exe), WebP (cwebp.exe, dwebp.exe), PAQ8L (paq-8l_intel.exe), PAQ8P (paq8p_sse2.exe), JPEG-LS (jpeg.exe), JPEG2000 (convert.exe), PNG (opting.exe) are tested on a set of 19 grayscale images and 10 color images (widespread images used in testing compression methods). An image compression program tester was developed. The tester receives images and executable files of image compression programs, and programs are started for each input image to compress and restore the image. The program operation results are contained in an HTML/CSS file, which includes, among other information, the bitrates achieved by the compression programs and the results of checking how successfully the compressed files were restored. Partial clones of the Blend-A13+, Blend-16, Blend-20 compression methods have been made to compare the effectiveness of the multipredictors that lie at the heart of the Blend-A13+, Blend-16, Blend-20 methods. Partial clones of the Blend-A13+, Blend-16, Blend-20 compression methods consist of multipredictors used in the Blend-A13+, Blend-16, Blend-20, methods, and 13 and 16 elementary predictors used in the Blend-A13+ and Blend-16 methods, respectively. The predictor GAP+ is replaced by the predictor GAP; the modeling like JPEG-LS is replaced by contextual modeling with quantization of a context from differences of pixels from the vicinity of the coded pixel, the arithmetic coder, and the reversible intercolor RGB-YUV transformation from JPEG2000. For many images, the obtained partial clones outperformed the results yielded by the JPEG-LS, JPEG2000, PNG methods and gave results at the level provided by the PAQ8L and WebP compression methods. In general, the BMF2.01 method demonstrated the best results on the test set of images. On the test set of images, the multipredictors from Blend-16 and Blend-20 unexpectedly provided color image compression results poorer than the multipredictor from the Blend-A13+ method. In compressing grayscale images, the multipredictor from Blend-20 yielded better results than the multipredictors from Blend-16, Blend-A13+.