2009•Unpublished venueRequires access

Accurate H.264 rate control with new rate-distortion models

Yih Han Tan, Zhengguo G. Li, Susanto Rahardja

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Abstract

H.264 rate control is an interesting problem that has motivated numerous possible solutions. The challenge lies in determining a quantization parameter Qp that will be used for both the rate-distortion (R-D) optimization process and the quantization of transform coefficients. In this work, we attempt to achieve effective rate control with a different approach. By modelling the relationships of distortion, texture bits, non-texture bits and Qp, we can derive the Qp required for both R-D optimization and quantization through Lagrangian optimization. From experiments with several video sequences, we found that our rate control scheme is capable of effective rate control with minimal model updates during encoding. The proposed rate control scheme adapts quickly to the characteristic of the source data and is particularly effective at controlling the rate of videos with high and unpredictable motion content.

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What this paper is about

H.264 rate control is an interesting problem that has motivated numerous possible solutions. The challenge lies in determining a quantization parameter Qp that will be used for both the rate-distortion (R-D) optimization process and the quantization of transform coefficients. In this work, we attempt to achieve effective rate control with a different approach. By modelling the relationships of distortion, texture bits, non-texture bits and Qp, we can derive the Qp required for both R-D optimization and quantization through Lagrangian optimization. From experiments with several video sequences, we found that our rate control scheme is capable of effective rate control with minimal model updates during encoding. The proposed rate control scheme adapts quickly to the characteristic of the source data and is particularly effective at controlling the rate of videos with high and unpredictable motion content.

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Available abstract

H.264 rate control is an interesting problem that has motivated numerous possible solutions. The challenge lies in determining a quantization parameter Qp that will be used for both the rate-distortion (R-D) optimization process and the quantization of transform coefficients. In this work, we attempt to achieve effective rate control with a different approach. By modelling the relationships of distortion, texture bits, non-texture bits and Qp, we can derive the Qp required for both R-D optimization and quantization through Lagrangian optimization. From experiments with several video sequences, we found that our rate control scheme is capable of effective rate control with minimal model updates during encoding. The proposed rate control scheme adapts quickly to the characteristic of the source data and is particularly effective at controlling the rate of videos with high and unpredictable motion content.

Key concepts: Quantization (signal processing), Rate distortion, Rate–distortion optimization, Computer science, Rate–distortion theory, Distortion (music), Encoding (memory), Algorithm

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