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Related Experiment Video

Updated: Jul 7, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Joint source-channel coding for motion-compensated DCT-based SNR scalable video.

Lisimachos P Kondi¹, Faisal Ishtiaq, Aggelos K Katsaggelos

¹Department of Electrical Engineering, State University of New York at Buffalo, Amherst, NY 14260, USA. lkondi@eng.buffalo.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|February 6, 2008

Summary

This summary is machine-generated.

This study introduces a joint source-channel coding method for scalable video transmission. The approach optimizes bit allocation across layers to minimize distortion, enhancing video quality over wireless channels.

Related Experiment Videos

Last Updated: Jul 7, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Area of Science:

Video Coding and Transmission
Information Theory
Digital Communications

Background:

Scalable video coding enables adaptable transmission over varying network conditions.
Joint source-channel coding is crucial for optimizing video quality under channel impairments.
Motion-compensated DCT-based coding and H.263+ SNR scalable codec are standard techniques.

Purpose of the Study:

To develop an optimal framework for joint source-channel coding in scalable video transmission.
To minimize overall video distortion by optimally allocating bit rates across scalable layers.
To investigate the sensitivity of video codecs to channel errors.

Main Methods:

Developed a framework for optimal selection of source and channel coding rates across scalable layers.
Utilized experimentally obtained universal rate-distortion characteristics.
Allocated bit rates between scalable layers and within each layer for source and channel coding.
Employed H.263 Version 2 SNR scalable codec and rate-compatible punctured convolutional (RCPC) codes.

Main Results:

Demonstrated an algorithm for optimal rate allocation that minimizes distortion.
Evaluated performance over wireless channels, considering various channel conditions and coding parameters.
Showcased the effectiveness of the proposed approach for H.263+ SNR scalable video transmission.

Conclusions:

The proposed joint source-channel coding approach effectively minimizes distortion in scalable video transmission.
Optimal bit rate allocation is critical for robust video delivery over wireless channels.
The method provides a framework for adapting to different channel conditions and coding configurations.