Macroblock-Level Adaptive Scan Scheme for Discrete Cosine Transform Coefficients
Discrete cosine transform (DCT) has been widely used in image/video coding systems, where zigzag scan is usually employed for DCT coefficient organization. However, due to local diversity of prediction errors, the fixed scan pattern, such as the traditional zigzag scan, is not efficient all the time for organizing the DCT transformed prediction errors. In this paper, the statistical distribution of prediction residuals is studied first, and two other scan patterns are introduced to complete the set of possible patterns. Then, a new adaptive scheme that adaptively chooses for each macroblock the best scan pattern to arrange the quantized DCT coefficients into arrays is presented. The best scan pattern is selected for each macroblock according to its own residual characteristics and hence leads to improved coding efficiency. Experimental results show that this macroblock-level adaptive scan scheme (MLASS) can always outperform the traditional zigzag one and the peak signal to noise ratio (PSNR) gain can be up to 0.65dB. Moreover, the extra two scan patterns do not increase memory requirement much. Additionally, a fast scan pattern decision algorithm is given for complexity reduction. This fast version of MLASS can keep almost the same coding efficiency while with much lower complexity.