Modeling of rate and perceptual quality of video and its application to frame rate adaptive rate control

Zhan Ma, Meng Xu, Kyeong Yang, Yao Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In a prior work, we have developed both rate and perceptual quality models for temporal and amplitude (i.e., SNR) scalable video produced by the H.264/SVC encoder. In this paper, we validate from experimental data that the functional form of the rate model is applicable to H.264/AVC encoded video, which has the same temporal scalability but no SNR scalability, but the model parameter values differ. We further investigate how to predict both rate and quality model parameters using content features computed from the original video. Experimental data show that with proper feature combination, we can estimate the model parameters very accurately, and the estimated bit rate and quality using the predicted model parameters match with the measured bit rate and quality with high Pearson correlation (PC) and small root mean square error (RMSE). We have implemented a simple pre-processor in the H.264/AVC encoder to guide the frame rate adaptive rate control. Results show that our model-based frame rate adaptive rate control outperforms the default rate control algorithm with better quality.

Original languageEnglish (US)
Title of host publicationICIP 2011
Subtitle of host publication2011 18th IEEE International Conference on Image Processing
Pages3321-3324
Number of pages4
DOIs
StatePublished - 2011
Event2011 18th IEEE International Conference on Image Processing, ICIP 2011 - Brussels, Belgium
Duration: Sep 11 2011Sep 14 2011

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Other

Other2011 18th IEEE International Conference on Image Processing, ICIP 2011
CountryBelgium
CityBrussels
Period9/11/119/14/11

Keywords

  • H.264/AVC
  • Rate model
  • frame rate adaptive rate control
  • perceptual quality model

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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