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A Novel Rate Control Method for H.264/AVC Based on
Frame Complexity and Importance
Haibing Chen 1 , Mei Yu 1,2 , Feng Shao 1 , Zongju Peng 1 , Fucui Li 1 ,
and Gangyi Jiang 1,2
1 Faculty of Information Science and Engineering, Ningbo University,
Ningbo-city, 315211, China
2 National Key Lab of Software New Technology, Nanjinging University,
Nanjing-city, 210093, China
jianggangyi@126.com
Abstract. In this paper, we present a new rate-control algorithm based on frame
complexity and importance (CI) for H.264/AVC video coding. The proposed
method aims at selecting accurate quantization parameters for inter-coded
frames according to the target bit rates, by accurately predicting frame CI using
the statistics of previously encoded frames. Bit budget is allocated to each
frame adaptively updated according to its CI, combined with the buffer status.
We compare the proposed method with JVT-G012 used by H.264/AVC with
the software JM10.1. The PSNR performance of video coding is improved by
the proposed method from 0.142 to 0.953 dB, and the BDPSNR performance is
improved from 0.248 to 0.541dB. The proposed method can also provide more
consistent visual quality and alleviated sharp drops for frames caused by high
motions or scene changes with the PSNR standard deviation decreases from
0.134 to 1.514dB.
1 Introduction
The remarkable evolution of video coding technology has underlined the development
of a multitude of novel signal compression techniques that aimed to optimise the
compression efficiency and quality of service of standard video coders under certain
bandwidth [1]. Rate control plays a critical role in the video encoder, although it does
not belong to the normative part in video coding standards. It regulates the coded bit
stream to satisfy certain given conditions, on the one hand, and enhances the quality
of coded video, on the other hand. Some efficient rate control schemes have been
proposed and used, such as TM5 for MPEG-2 [2], TMN8 for H.263 [3], VM8 for
MPEG-4 [4] and JVT-G012 for H.264/AVC [5].
JVT-G012 uses a fluid flow traffic model to compute the target bit for the current
encoding frame and a linear model to predict mean absolute difference (MAD) to
solve the chicken and egg dilemma. Lee et al. presented an complexity-based intra-
frame rate control algorithm [6], by predicting a relative complexity of a current
macroblock (MB) from complexities of its spatially/temporally neighboring MBs.
Jing et al. presented an effect of I frame RC by using gradient-based image complex-
ity and exponential R-Qstep model [7]. Zhu et al. used temporal average MAD to
replace traditional MAD linear prediction model, increasing the average luminance
 
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