Performance-Complexity Analysis of High Resolution Video Encoder and its Memory Organization for DSP Implementation

Yang, Zhigang; Gao, Wen; Liu, Yan

doi:10.1109/icme.2006.262767

Cited by 7 publications

(1 citation statement)

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“…As the experimental results have shown that two fixed-size block levels are generally good for video coding [31] and video deinterlacing [16], we propose a three-leveled block strategy that has two fixed-size block levels and one variable-size block level to achieve better prediction and motion compensation. In this paper, a motion-compensated deinterlacing scheme is presented based on hierarchical motion analysis, which is incarnated by the novel three-leveled block strategy.…”

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confidence: 99%

Deinterlacing Using Hierarchical Motion Analysis

Huang

Zhao

et al. 2010

IEEE Trans. Circuits Syst. Video Technol.

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Abstract-A motion-compensated deinterlacing scheme based on hierarchical motion analysis is presented. According to deinterlacing steps, our contribution can be divided into four parts: motion estimation, motion state analysis, motion consistency analysis, and finer-grained interpolation. In motion estimation, we introduce a Gaussian noise model for choosing the best motion vector for each block, and make a tradeoff between utilizing previous de-interlaced frames and avoiding error propagation. A directional interpolation method is also introduced in this part for backward fields. In motion state analysis, we define two motion states for each pixel, thus achieve a compromise between traditional block-based strategies and the extreme pixel-based case. In motion consistency analysis, we propose to measure both the motion vector consistency and the motion state consistency in order to determine whether the previous two parts should be performed again with a different block size. In finer-grained interpolation, we utilize a combination of recursive median filters to generate the final results. Experimental results show that all of the proposed techniques are effective, either objectively or subjectively. As a result, we can achieve much higher image quality, with an average gain of about 1.83 dB in terms of peak signal-to-noise ratio. Moreover, the increased computation complexity is marginal.

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confidence: 99%