Image compression with QM-AYA adaptive binary arithmetic coder

1993 ◽  
Author(s):  
Joe-Ming Cheng ◽  
Glen G. Langdon, Jr.
Keyword(s):  
Image Compression ◽  
Binary Arithmetic ◽  
Arithmetic Coder

An overview of the basic principles of the Q-Coder adaptive binary arithmetic coder

1988 ◽  
Vol 32 (6) ◽  
pp. 717-726 ◽  
Author(s):  
W. B. Pennebaker ◽  
J. L. Mitchell ◽  
G. G. Langdon ◽  
R. B. Arps
Keyword(s):  
Basic Principles ◽  
Binary Arithmetic ◽  
Arithmetic Coder

Testing of Image Compression Methods

Vestnik MEI ◽  
2021 ◽  
pp. 105-113
Author(s):  
Pavel A. Chernov
Keyword(s):  
Image Compression ◽  
Color Image ◽  
Input Image ◽  
Test Set ◽  
Other Information ◽  
Contextual Modeling ◽  
Operation Results ◽  
Arithmetic Coder ◽  
Color Image Compression ◽  
Grayscale Images

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+.

scholarly journals Adaptive Binary Arithmetic Coder-Based Image Feature and Segmentation in the Compressed Domain

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Hsi-Chin Hsin ◽  
Tze-Yun Sung ◽  
Yaw-Shih Shieh ◽  
Carlo Cattani
Keyword(s):  
Computational Cost ◽  
Rate Distortion ◽  
Image Feature ◽  
Compressed Domain ◽  
Boundary Displacement ◽  
Binary Arithmetic ◽  
Efficient Scheme ◽  
Band Limited ◽  
Arithmetic Coder ◽  
Efficient Transmission

Image compression is necessary in various applications, especially for efficient transmission over a band-limited channel. It is thus desirable to be able to segment an image in the compressed domain directly such that the burden of decompressing computation can be avoided. Motivated by the adaptive binary arithmetic coder (MQ coder) of JPEG2000, we propose an efficient scheme to segment the feature vectors that are extracted from the code stream of an image. We modify the Compression-based Texture Merging (CTM) algorithm to alleviate the influence of overmerging problem by making use of the rate distortion information. Experimental results show that the MQ coder-based image segmentation is preferable in terms of the boundary displacement error (BDE) measure. It has the advantage of saving computational cost as the segmentation results even at low rates of bits per pixel (bpp) are satisfactory.

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