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This chapter will discuss the two main terrestrial television broadcasting systems widely used in China. One is the China Terrestrial Television Broadcasting (CTTB) standard which is named as ˵Frame structure, channel coding and modulation for digital television terrestrial broadcasting system″. It was issued in August 2006 as a mandatory standard for traditional terrestrial broadcasting and had been put into execution from August 2007. The other is China Multimedia Mobile Broadcasting (CMMB) which is a mobile television and multimedia standard developed and specified by the State Administration of Radio, Film and Television (SARFT). Based on the statellite and terrestrial interactive multiservice infrastructure (STiMi), it is a mixed satellite and terrestrial wireless broadcasting system designed to provide audio, video and data service for handheld receivers with less than 7 inch wide LCD display. The first part focuses on CTTB standard. The comparisons with ATSC and DVB-T standard and the considerations on primary working modes selection are discussed. The receiver design for both single carrier and multi-carrier options is also addressed. Then the applications of CTTB, including fixed, mobile and portable reception is presented. Especially, the signal coverage scheme for railway lines is introduced. As for the CMMB standard, this chapter firstly introduces the system infrastructure. Then its key techniques, combined with the comparison with another popular mobile TV standard DVB-H are presented. At last, the development of CMMB services in China is briefly depicted. [ABSTRACT FROM AUTHOR]

We adopt two priors to realize reflection separation from a single image, namely spatial smoothness, which is based on pixels΄ color dependency, and structure difference, which is got from different source images (transmitted image and reflected image) and different color channels of the same image. By analysing the optical model of reflection, we simplify the mixing matrix further and realize the method for getting spatially varying mixing coefficients. Based on the priors and using Gibbs sampling and appropriate probability density with Bayesian framework, our approach can achieve impressive results for many real world images that corrupted with reflections. [ABSTRACT FROM AUTHOR]

A novel semiautomatic colorization method is proposed based on quaternionic phase reconstruction. In this method, each color pixel is represented as a quaternion, whose polar magnitude and polar phase are recovered from the intensity of original grayscale image and color scribbles of user΄s manual input, respectively. To conduct structure-preserving colorization, color diffusion is restrained across global image structures, which are extracted using hierarchical edge representation along with structural importance measurement. To identify local spatial relationship between neighboring pixels, Gabor wavelets are applied to compute the similarity of local phase patterns. Our method is highlighted in well preserving image structures during colorization, where the color image is acquired by solving a linearly constrained quadratic optimization problem. Specifically, we develop a method to guide the user to scribble on the monochrome image, so that effective color propagation from less manual input can be expected. Experimental results demonstrate that our colorization method outperforms the state-of-the-art method in structural preservation and relatively better colorization results are available if the proposed rule of scribble user guidance is adopted. [ABSTRACT FROM AUTHOR]

The estimation of human age from face images has great potential in real-world applications. However, how to discover the intrinsic aging trend is still a challenging problem. In this work, we proposed a general distance metric learning scheme for regression problems, which utilizes not only data themselves, but also their corresponding labels to strengthen the credibility of distances. This metric could be learned by solving an optimization problem. Through the learned metric, it is easy to find the intrinsic variation trend of data by a relative small amount of samples without any prior knowledge of the structure or distribution of data. Furthermore, the test data could be projected to this metric by a simple linear transformation and it is easy to be combined with manifold learning algorithms to improve the performance. Experiments are conducted on the public available FG-NET database by gaussian process regression in the learned metric to validate our methods and the age estimation performance is improved over the traditional regression methods. [ABSTRACT FROM AUTHOR]

In applications of behavior recognition, the use of spatiotemporal invariant feature points can improve the robustness to noise, illumination and geometric distortions. In this paper, we develop a novel detection model of spatiotemporal invariant feature by generalizing the notion of image phase congruency to video volume phase congruency. The proposed model detects feature points by measuring the spatiotemporal phase congruency of Fourier series components along with their characteristic scale and principal orientation. Compared with other state-of-the-art methods, the key advantages of this interest point detector include the invariance to contrast variations and more precise feature location. Furthermore, an invariant feature descriptor is advanced based on the phase congruency map, resulting in enhanced discriminative power in classification tasks. Experimental results on KTH human motion dataset demonstrate the validity and effectiveness of the extracted invariant features in the human behavior recognition scheme. [ABSTRACT FROM AUTHOR]