JOURNAL ARTICLE

Low-latency video transmission over lossy packet networks using rate-distortion optimized reference picture selection

Liang YueMarkus FlierlBernd Girod

Year: 2003 Journal:   Proceedings - International Conference on Image Processing Vol: 2 Pages: II-181   Publisher: Institute of Electrical and Electronics Engineers

Abstract

Today's Internet video streaming systems employ buffering and multiple retransmissions to guarantee the correct reception of each packet, which leads to high latency in media delivery. In this work, a novel scheme of dynamic management of the dependency across packets is proposed using optimal reference picture selection at the frame level that adapts to the channel. The optimal selection of the reference picture is achieved with a rate-distortion framework, which minimizes the expected end-to-end distortion given a rate constraint. The expected distortion is calculated based on an accurate binary tree modeling with the effects of channel loss and error concealment taken into account. Experiments demonstrate that the proposed :scheme provides significant performance gains over a simple INTRA-insertion scheme. The increased error-resilience eliminates the need of retransmission, which makes it possible to reduce the latency from 10-15 seconds to a few hundred milliseconds, with similar video quality maintained.

Keywords:
Retransmission Computer science Network packet Real-time computing Latency (audio) Lossy compression Distortion (music) Video quality Computer network Channel (broadcasting) Algorithm Bandwidth (computing) Telecommunications Artificial intelligence

Metrics

29
Cited By
4.91
FWCI (Field Weighted Citation Impact)
8
Refs
0.96
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Video Coding and Compression Technologies
Physical Sciences →  Computer Science →  Signal Processing
Image and Video Quality Assessment
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Advanced Data Compression Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition

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