Modeling of rate and perceptual quality of compressed video as functions of frame rate and quantization stepsize and its applications

Zhan Ma, Meng Xu, Yen Fu Ou, Yao Wang

Research output: Contribution to journalArticle

Abstract

This paper first investigates the impact of frame rate and quantization on the bit rate and perceptual quality of compressed video. We propose a rate model and a quality model, both in terms of the quantization stepsize and frame rate. Both models are expressed as the product of separate functions of quantization stepsize and frame rate. The proposed models are analytically tractable, each requiring only a few content-dependent parameters. The rate model is validated over videos coded using both scalable and nonscalable encoders, under a variety of encoder settings. The quality model is validated only for a scalable video, although it is expected to be applicable to a single-layer video as well. We further investigate how to predict the model parameters using the content features extracted from original videos. Results show accurate bit rate and quality prediction (average Pearson correlation <0.99) can be achieved with model parameters predicted using three features. Finally, we apply rate and quality models for rate-constrained scalable bitstream adaptation and frame rate adaptive rate control. Simulations show that our model-based solutions produce better video quality compared with conventional video adaptation and rate control.

Original languageEnglish (US)
Article number6086602
Pages (from-to)671-682
Number of pages12
JournalIEEE Transactions on Circuits and Systems for Video Technology
Volume22
Issue number5
DOIs
StatePublished - 2012

Keywords

  • Content feature
  • H.264/AVC
  • perceptual quality model
  • rate control
  • rate model
  • scalable video adaptation
  • scalable video coding (SVC)

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Media Technology

Cite this

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title = "Modeling of rate and perceptual quality of compressed video as functions of frame rate and quantization stepsize and its applications",
abstract = "This paper first investigates the impact of frame rate and quantization on the bit rate and perceptual quality of compressed video. We propose a rate model and a quality model, both in terms of the quantization stepsize and frame rate. Both models are expressed as the product of separate functions of quantization stepsize and frame rate. The proposed models are analytically tractable, each requiring only a few content-dependent parameters. The rate model is validated over videos coded using both scalable and nonscalable encoders, under a variety of encoder settings. The quality model is validated only for a scalable video, although it is expected to be applicable to a single-layer video as well. We further investigate how to predict the model parameters using the content features extracted from original videos. Results show accurate bit rate and quality prediction (average Pearson correlation <0.99) can be achieved with model parameters predicted using three features. Finally, we apply rate and quality models for rate-constrained scalable bitstream adaptation and frame rate adaptive rate control. Simulations show that our model-based solutions produce better video quality compared with conventional video adaptation and rate control.",
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author = "Zhan Ma and Meng Xu and Ou, {Yen Fu} and Yao Wang",
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N2 - This paper first investigates the impact of frame rate and quantization on the bit rate and perceptual quality of compressed video. We propose a rate model and a quality model, both in terms of the quantization stepsize and frame rate. Both models are expressed as the product of separate functions of quantization stepsize and frame rate. The proposed models are analytically tractable, each requiring only a few content-dependent parameters. The rate model is validated over videos coded using both scalable and nonscalable encoders, under a variety of encoder settings. The quality model is validated only for a scalable video, although it is expected to be applicable to a single-layer video as well. We further investigate how to predict the model parameters using the content features extracted from original videos. Results show accurate bit rate and quality prediction (average Pearson correlation <0.99) can be achieved with model parameters predicted using three features. Finally, we apply rate and quality models for rate-constrained scalable bitstream adaptation and frame rate adaptive rate control. Simulations show that our model-based solutions produce better video quality compared with conventional video adaptation and rate control.

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