This paper presents an improved rate control method for H.264. First, the scene changes are detected by the average absolute difference of the brightness histograms between the adja-cent frames. Then, the bit allocation and quantization parameters are adjusted, using a certain threshold. In addition, the calculation of the mean absolute difference (MAD) is modified in an alterna-tive way, which makes the rate distortion optimization (RDO) more accurate. Extensive simulation results show that the pro-posed method, compared with G012, can improve the average peak signal-to-noise ratio (PSNR) and moderate the image quality.
To study the scene classification in the Synthetic Aperture Radar (SAR) image, a novel method based on kernel estimate, with the Markov context and Dempster-Shafer evidence theory is proposed. Initially, a nonparametric Probability Density Function (PDF) estimate method is introduced, to describe the scene of SAR images. And then under the Markov context, both the determinate PDF and the kernel estimate method are adopted respectively, to form a primary classification. Next, the primary classification results are fused using the evidence theory in an unsupervised way to get the scene classification. Finally, a regularization step is used, in which an iterated maximum selecting approach is introduced to control the fragments and modify the errors of the classification. Use of the kernel estimate and evidence theory can describe the complicated scenes with little prior knowledge and eliminate the ambiguities of the primary classification results. Experimental results on real SAR images illustrate a rather impressive performance.
A new blind equalization algorithm based on the modi- fied constant modulus algorithm (MCMA) and dithered signed- error constant modulus algorithm (DSE-CMA) is proposed. This dithered signed-error MCMA (DSE-MCMA) can not only reduce the computational complexity, but also recover the phase rotation in the complex channel. Simulation results have verified the analysis and indicated the good property of DSE-MCMA.
