Abstract
Binary MQ arithmetic coding is widely used as a basic entropy coder in multimedia coding system. MQ coder esteems high in compression efficiency to be used in JBIG2 and JPEG2000. The importance of arithmetic coding is increasing after it is adopted as a unique entropy coder in HEVC standard. In the binary MQ coder, arithmetic approximation without multiplication is used in the process of recursive subdivision of range interval. Because of the MPS/LPS exchange activity that happens in the MQ coder, the output byte tends to increase. This paper proposes an enhanced binary MQ arithmetic coder to make use of look-up table (LUT) for (A × Qe) using quantization skill to improve the coding efficiency. Multi-level quantization using 2-level, 4-level and 8-level look-up tables is proposed in this paper. Experimental results applying to binary documents show about 3% improvement for basic context-free binary arithmetic coding. In the case of JBIG2 bi-level image compression standard, compression efficiency improved about 0.9%. In addition, in the case of lossless JPEG2000 compression, compressed byte decreases 1.5% using 8-level LUT. For the lossy JPEG2000 coding, this figure is a little lower, about 0.3% improvement of PSNR at the same rate.
Highlights
Since Shannon announced in 1948 that a message can be expressed with the smallest bits based on the probability of occurrence, many studies on entropy coding have been conducted
As explained in related works, binary MQ coding is divided into Most Probable Symbol (MPS) and Least Probable Symbol (LPS) and interval segmentation is performed by approximating (A × Qe ) to Qe under the assumption that A is close to 1
Arithmetic coder, instead of (A × Qe ) as shown in Equations (3) and (4), it is substituted with Qe like Equations (5) and (6) on the assumption that A is close to 1
Summary
Since Shannon announced in 1948 that a message can be expressed with the smallest bits based on the probability of occurrence, many studies on entropy coding have been conducted. BAC with adaptive probability estimation process was developed to improve coding performance compared with existent MQ-coder and M-coder. Encoder–decoder complexity is quite increasing with a little bit savings [14,25] As another adaptive BAC, the adaptive binary range coder (ABRC) [15] uses virtual sliding window (VSW) [26] for probability estimation, which does not require look-up tables. The VSW estimation provides a faster probability adaptation at the initial encoding/decoding stage and especially more precise probability estimation for very low entropy binary sources It needs multiplication operations and more complex encoder—decoder processing. Because computing power has enormously increased afterward, there are lots of room for improvement of the approximation method By this motivation, in this paper, using the existent probability estimation table Qe , multi-level quantization tables of (A × Qe ) are proposed to be used as look-up table (LUT).
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