Kui Zhang

Bio: Kui Zhang is an academic researcher from University of Surrey. The author has contributed to research in topics: Motion estimation & Motion compensation. The author has an hindex of 7, co-authored 10 publications receiving 176 citations.

Variable block size video coding with motion prediction and motion segmentation

Abstract: In the paper we are concerned with the efficient coding of image sequences for video- conference applications. In such sequences, large image regions usually undergo a uniform translational motion. Consequently, to maximize the coding efficiency and quality, the codec should be able to segment and estimate multiple translational motions accurately and reliably. Following the above premise, we propose an algorithm which combines several known and new techniques. Firstly, a traditional variable block size motion compensation was used, but employing a novel robust motion estimation algorithm. The algorithm can estimate multiple motions to a sub-pixel accuracy and also provides a reliable motion segmentation. Whenever there exist multiple motions within a block, the motion boundary is recovered and approximated by a straight line. Also, an inter-block motion prediction is used to achieve a further improvement of the compression ratio. A comparison with the H.261 scheme shows that the proposed algorithm produces better results both in terms of PSNR and bit-rate. To judge the contribution of the motion segmentation to the overall performance, experiments have been carried out with a variant of the algorithm where only single motion within any block is allowed. This incapacitated variant emulates a commonly used approach for variable block size coding. The comparison of the proposed and incapacitated variants shows that the use of motion segmentation can lower the bit rate and deliver a better visual quality of the reconstructed image sequence.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Image sequence coding using multiple-level segmentation and affine motion estimation

Abstract: A very low bit-rate video codec using multiple-level segmentation and affine motion compensation is presented. The translational motion model is adequate to motion compensate small regions even when complex motion is involved; however, it is no longer capable of delivering satisfactory results when applied to large regions or the whole frame. The proposed codec is based on a variable block size algorithm enhanced with global motion compensation, inner block segmentation, and a set of motion models used adaptively in motion compensation. The experimental results show that the proposed method gives better results in terms of the bit rate under the same PSNR constraint for most of the tested sequences as compared with the fixed block size approach and traditional variable block size codec in which only translational motion compensation is utilized.

Global motion estimation and robust regression for video coding

Abstract: In the H.263 Version 2 (H.263+) coding standard, the global motion compensation can be introduced by using Reference Picture Resampling (Annex. P) syntax. Such an application requires that the global motion parameters be estimated automatically. We propose a global motion estimation algorithm based on the Taylor expansion equation and robust regression technique using probabilistic thresholding. The experimental results confirm that the proposed algorithm can improve both the coding efficiency and the quality of motion compensation on sequences involving camera movement.

Video coding using affine motion compensated prediction

Abstract: The aim in variable block size and object based video coding is to motion compensate as large regions as possible. Whereas the translational motion model is adequate to motion compensate small regions even if the image sequence involves complex motion, it is no longer capable of delivering satisfactory results for large regions. A more sophisticated motion model is required in such regions. In this paper, we extend our previous approach by proposing a multiple layer video codec which uses affine model for large regions to improve motion compensation. The translational model is still used for small regions to reduce the bit overhead. A multi-resolution robust Hough transform based motion estimation technique is used to estimate the affine motion parameters. The experimental results show that the proposed method gives better results in terms of the bit rate under the same PSNR constraint for most of the tested sequences as compared with our previous approach and an implementation of the standard H.261.

Motion based image segmentation for video coding

Abstract: A robust and stable scene segmentation is a prerequisite for the object based coding. Various approaches to this complex task have been proposed, including segmentation of optic flow, grey-level based segmentation or simple division of the scene into moving and stationary regions. In this paper, we propose an algorithm which combines all three approaches in order to get a more robust and accurate segmentation of the moving objects. The experimental results show that the proposed algorithm can significantly reduce over-segmentation and maintain accurate motion boundaries. The use of the proposed approach in video coding can increase the PSNR and reduce the bit rate.

Multi-mode region-of-interest video object segmentation

Abstract: The disclosure is directed to techniques for automatic segmentation of a region-of-interest (ROI) video object from a video sequence. ROI object segmentation enables selected ROI or “foreground” objects of a video sequence that may be of interest to a viewer to be extracted from non-ROI or “background” areas of the video sequence. Examples of a ROI object are a human face or a head and shoulder area of a human body. The disclosed techniques include a hybrid technique that combines ROI feature detection, region segmentation, and background subtraction. In this way, the disclosed techniques may provide accurate foreground object generation and low-complexity extraction of the foreground object from the video sequence. A ROI object segmentation system may implement the techniques described herein. In addition, ROI object segmentation may be useful in a wide range of multimedia applications that utilize video sequences, such as video telephony applications and video surveillance applications.

Modeling visual attention's modulatory aftereffects on visual sensitivity and quality evaluation

Abstract: With the fast development of visual noise-shaping related applications (visual compression, error resilience, watermarking, encryption, and display), there is an increasingly significant demand on incorporating perceptual characteristics into these applications for improved performance. In this paper, a very important mechanism of the human brain, visual attention, is introduced for visual sensitivity and visual quality evaluation. Based upon the analysis, a new numerical measure for visual attention's modulatory aftereffects, perceptual quality significance map (PQSM), is proposed. To a certain extent, the PQSM reflects the processing ability of the human brain on local visual contents statistically. The PQSM is generated with the integration of local perceptual stimuli from color contrast, texture contrast, motion, as well as cognitive features (skin color and face in this study). Experimental results with subjective viewing demonstrate the performance improvement on two PQSM-modulated visual sensitivity models and two PQSM-based visual quality metrics.

Video Saliency Incorporating Spatiotemporal Cues and Uncertainty Weighting

Abstract: We propose a novel algorithm to detect visual saliency from video signals by combining both spatial and temporal information and statistical uncertainty measures. The main novelty of the proposed method is twofold. First, separate spatial and temporal saliency maps are generated, where the computation of temporal saliency incorporates a recent psychological study of human visual speed perception. Second, the spatial and temporal saliency maps are merged into one using a spatiotemporally adaptive entropy-based uncertainty weighting approach. The spatial uncertainty weighing incorporates the characteristics of proximity and continuity of spatial saliency, while the temporal uncertainty weighting takes into account the variations of background motion and local contrast. Experimental results show that the proposed spatiotemporal uncertainty weighting algorithm significantly outperforms state-of-the-art video saliency detection models.

Method for encoding and decoding video information, a motion compensated video encoder and a corresponding decoder

Abstract: A motion compensated video coding method which can be applied especially in transfer of video streams using low transmission bit rate is presented. In the motion compensated coding method, the motion of picture elements between a piece of reference video information and a piece of current video information is estimated and then modeled using certain basis function and coefficients. The coefficients are quantized, and the quantizer is selected according to a certain selection criterion, for example, based on a target image quality or on a target transmission bit rate. Preferably the selection criterion is such that it automatically adjust the accuracy with which the motion of picture elements is represented to be related to the accuracy with which the prediction error information is represented. A decoding method, an encoder and a corresponding decoder are also described.

Multi-Frame Motion-Compensated Prediction for Video Transmission

Abstract: Preface. Introduction. 1. State-Of-The-Art Video Transmission. 2. Rate-Constrained Coder Control. 3. Long-Term Memory Motion-Compensated Prediction. 4. Affine Multi-Frame Motion-Compensated Prediction. 5. Fast Motion Estimation for Multi-Frame Prediction. 6. Error Resilient Video Transmission. 7. Conclusions. References. Index.