Mr. Xiwen Zhao Profile

Xiwen Zhao

at Univ of Missouri Columbia

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Publications (2)

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Proceedings Article | 19 January 2009 Paper

Local structure learning and prediction for efficient image compression

Xiwen Zhao, Zhihai He

Proceedings Volume 7257, 72570Y (2009) https://doi.org/10.1117/12.805953

KEYWORDS: Image compression, JPEG2000, Linear filtering, Computer programming, Discrete wavelet transforms, Transform theory, Wavelets, Image classification, Image transmission, Image filtering

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One major challenge in image compression is to efficiently represent and encode high-frequency structural components in images, such as edges, contours, and texture regions. To address this issue, we propose a scheme to learn local image structures and efficiently predict image data based on this structure information. We prove that, using singular value decomposition, we can find a small number of basis vectors whose linear combinations are able to closely approximate local image patches. By extrapolating these linear combination coefficients, we can efficiently predict neighboring pixels of the local image patch. We find that this structure learning and prediction procedure is very efficient for image regions with significant structural components. However, because of its large overhead and high computational complexity, its performance degrades in other image regions, such as smooth areas. Therefore, we propose a classification scheme to partition an image into three types of regions: structure regions, non-structure regions, and transition regions. Structure regions are encoded with structure prediction. With image in-painting, the non-structure and transmission regions are extended into a maximally smoothed image which can be efficiently encoded with conventional image compression methods, such as JPEG2000. Our experimental results demonstrate that the proposed method outperform the state-of-the-art JPEG2000 image compression.

Proceedings Article | 19 January 2009 Paper

Content-aware packet scheduling for multi-session video streaming over wireless mesh networks

Yongfei Zhang, Shiyin Qin, Xiwen Zhao, Zhihai He

Proceedings Volume 7257, 725713 (2009) https://doi.org/10.1117/12.806009

KEYWORDS: Video, Distortion, Error analysis, Computer programming, Receivers, Quantization, Coastal modeling, Performance modeling, Computer engineering, Molybdenum

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Packet scheduling is of great importance to the performance optimization of multi-session video streaming over wireless mesh networks. In this paper, we explore the time-varying characteristics of the input videos and propose a Content-and-Deadline-Aware Scheduling (CDAS) scheme for multi-session video streaming over wireless multi-hop networks. The basic idea of proposed scheduling scheme is to choose and transmit more packets with higher importance while meeting their stringent delay constraints, to maximize the constructed video quality at the receivers' side. More specifically, the packet schedule is determined in such a way that not only the backlog, but also the contributions to the reconstructed videos as well as the stringent delay constraints and time-varying network condition are all considered. This is enabled by our proposed priority model composed of two major components: content priority and scheduling priority. For the content priority, we develop a fast and efficient packet-level transmission distortion model to accurately predict the corresponding distortion for a lost packet at the encoder side. For scheduling priority, we consider delay requirement and dynamic network conditions of subsequent transmission links. Our extensive simulations demonstrate that the proposed transmission distortion model and the CDAS policy significantly improve the performance of multi-session video streaming over wireless mesh networks.

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