Mr. Xiwen Zhao Profile

Xiwen Zhao

at Univ of Missouri Columbia

SPIE Involvement:

Author

Publications (2)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

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

Read Abstract +

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

Read Abstract +

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.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years