An improved Basic-Unit Layer Rate-Control Scheme on H.264

Author(s):  
Su Shuguang ◽  
Yu Shengsheng ◽  
Zhou Jingli
2013 ◽  
Vol 2013 ◽  
pp. 1-6
Author(s):  
Xiao Chen ◽  
Haiying Liu

In the process of the video coding, special attention should be paid to the subjective quality of the image. In the JVT-G012 algorithm for H.264, the influence of the human visual characteristic in basic unit layer rate control was not taken into account. This paper takes the influence of the human visual characteristic into the full consideration and offers ways to improve the subjective quality of the image. The visual characteristic factor, which is constituted by the motion feature and edge feature, is used to reasonably allocate the target bits, and then its quantization parameter is adjusted by encoded frame information. The experimental results show that, in comparison to the original algorithm, the proposed algorithm can not only control the bit rate more accurately but also make the peak signal to noise ratio (PSNR) stable, so as to improve the stationarity of the video image. The subjective quality of the reconstructed video is more satisfying.


2013 ◽  
Vol 347-350 ◽  
pp. 844-848
Author(s):  
Hong Gao Zhu ◽  
Qi Feng

Rate control is the core issue to realize video encoder, and it is also one of the key factors determining the quality of video code system. Given the MAD (Mean Absolute Difference) prediction and the drawback of bit rate control in the BU (Basic Unit) layer, improvements are put forward on the basis of analyzing and researching the G012 rate control algorithms in this paper. In this paper, we proposed a new video bit rate control algorithm to overcome the drawback of bit rate control in the BU (Basic Unit) layer. Combined with the image brightness gradient value to estimate in the MAD prediction, the allocation encoding bits method based on the PSNR (Peak Signal to Noise Ratio) is proposed in the rate control of BU layer. The experiment results show that compared with JM algorithms in H.264 standard reference software, the improved algorithm is enhanced in the PSNR.


Author(s):  
Tao Yan ◽  
Anyuan Deng ◽  
Woei-Jiunn Tsaur ◽  
Jia-Hong Li ◽  
Hsin-Chieh Tsai

2010 ◽  
Vol 20 (2) ◽  
pp. 250-261 ◽  
Author(s):  
Chih-Hung Kuo ◽  
Li-Chuan Chang ◽  
Kuan-Wei Fan ◽  
Bin-Da Liu

2005 ◽  
Author(s):  
Do-Kyoung Kwon ◽  
Mei-Yin Shen ◽  
C.-C. Jay Kuo

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