Multiple description coding (MDC) is an efficient source coding technique for error-prone transmission over multiple channels. In this paper, we focus on the design of a new polyphase down-sampling based MDC (NPDS-MDC) for image signals. The encoding of our proposed NPDS-MDC consists of three steps. First, we perform down-sampling on each N×N image block according to the quincunx down-sampling pattern. Second, we propose a new transform and apply it to the down-sampled pixels to produce the side descriptions. Third, we develop an error compensation algorithm to reduce the compression distortion occurring on the down-sampled pixels. In our scheme, the side decoding is performed posterior to image interpolation with reference to the down-sampled compressed pixels. Moreover, the central decoding is achieved by interlacing the side descriptions. We also propose a compression-constrained central deblocking algorithm to further improve the efficiency of the central decoding. The experimental results indicate that our proposed MDC scheme offers clearly superior performance, especially at high bit rates, as compared to the state-of-the-art methods for various types of images.

In this paper, we design a new multiple description coding scheme for image signals based on our proposed compression-guided optimization. Firstly, we propose a compression-constrained adaptive filtering method to produce two descriptions for the source image, where the proposed filtering algorithm works not only to guarantee a high-quality side decoding but also make a high-efficient central decoding. Secondly, we design a compression-dependent deblocking algorithm based on the transform coefficients which are decoded from both descriptions to improve the performance for the cental decoding. Experimental results demonstrate that our proposed method achieves impressive performance gains when it is applied to image signals.

Coding of a color image usually happens in the YCbCr space so that the rate-distortion optimization is conducted in this space. Due to the use of a non-unitary matrix in the RGB-to-YCbCr conversion, an optimal coding performance achieved in the YCbCr space does not guarantee an optimal quality in the RGB space, which would impact most display devices that need RGB signals as the inputs. In this paper, we first study the relationship between the coding distortions of the compressed RGB signals and the quantization errors occurred in the coded YCbCr signals. Then, we design a new quantization scheme crossing the RGB and YCbCr spaces to achieve a high-quality color image compression with the YCbCr 4:4:4 format. Although our proposed quantization takes place in the YCbCr space, it aims at reducing the coding distortion in the RGB space as much as possible. Experimental results demonstrate that our proposed method offers a significant quality gain over the existing block-based coding methods for various images.

In this letter, we propose a minimum mean square error (MMSE) directed linear interpolation to compose the high-resolution image from a single low-resolution image. We build up our interpolation model by using some similar image patches selected according to the nonlocal geometric similarity. First, we use a two-stage search scheme to collect the matched patches inside the whole image. Second, a similarity scaling factor is used in the second search to refine the collected patches so as to help find a robust solution to the MMSE-directed interpolation. Third, our MMSE-directed interpolation is regularized by the involved reference patches to make the solved interpolation coefficients more reliable. Experimental results show that our proposed method outperforms the state-of-the-art MMSE-directed linear interpolation schemes and works competitively with the state-of-the-art learning-based ones.

ABSTRACT This paper proposes a new generative model for flexible editing of human motion. Different from previous work, three intuitive factors of motion, namely, content, identity and style, can be manipulated directly with the new model. With the new generative model, motion editing can be achieved in various aspects, including transferring an unknown style from an actor to another, synthesizing other styles for an unknown actor and generating a new motion with other content. Copyright © 2013 John Wiley & Sons, Ltd.

Many prior works aim to provide style editing approaches which are intuitive and straightforward, such as [Min et al. 2010] etc. But they did not consider the content editing, like generate a running motion for an actor given his walking motion. For the purpose of style editing, most researches use linear model, such as PCA, ICA and multilinear model [Min et al. 2010]. However, the human motion is actually a highly nonlinear model [Elgammal and Lee 2004]. But the nonlinear model can not construct a direct mapping which makes the reconstruction of high-dimensional data complex. Besides, it can only manipulate data in the training database. In addition, when extracting the style of motion, most methods do not separate the content before learning the style. This confuses the content and style when doing the style learning, and therefore impairs the final result.

No abstract available.

Taking the characteristics of mobile devices and the demands for graphics rendering into account, this paper gives a set of definitions of attributes to express the performance of mobile terminals and algorithm performance. And then, according to the relationship of these attributes, a point-based rendering architecture for mobile devices is proposed. It includes pre-processing simplification, data organization and rendering. Instead of just simplifying the algorithm on PC, the entire process takes the multi-attributes of mobile devices and algorithm into account. Experiments shows that it can make full use of the limited resources to carry out a relatively realistic and real-time rendering.

The simplification of point models is important in point-based processing technology because of the increasing of data complexity. Many researches focus on getting the subset from the initial point set, which can not represent the whole object properly. In this paper, we present a novel simplification algorithm based on Moving Least Square (MLS) and Splats for point models. The algorithm uses MLS to represent the point models and can get the minimum error point which is used to deputize its neighborhood. This approach can get new proper agent points instead of subset. Then we calculate the error based on the rendering results, which means considering the geometry of splat when calculating the error of simplification. Experiment results show that this algorithm is not only efficient, but also has good quality.