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A motion estimation algorithm was proposed using 2 bit-depth pixels. The reduction of pixel depth was first formalized by two successive steps, namely interval partitioning and interva mapping. The former is a many-to-one mapping which determines motion estimation performance, while the latter is a one-to-one mapping. A non-uniform quantization method was then presented to compute three initial thresholds of the interval partitioning. These initial thre-sholds were subsequently refined by using a membership function to solve the mismatch of pixel values near them caused by signal noise and so on. Afterwards, a matching criterion was discussed suitable for the motion estimation using 2 bit-depth pixels. A novel motion estimation algorithm was consequently addressed based on 2 bit-depth pixels and fuzzy quantiza-tion. To further predict the precision of the proposed algorithm, a bit resolution reduction error-motion vector precision model was built by exploiting the auto-correlation function. Extensive experimental results show that the proposed algo-rithm can always achieve high motion estimation precis rious characteristics, especially for those with detailed scene and complex motion. Compared with traditional 2 bit motion estimation, the proposed algorithm gains 0.27 dB improvement in terms of average peak signal-to-noise ratio of motion compensation.

A motion estimation algorithm was proposed using 2 bit-depth pixels. The reduction of pixel depth was first formalized by two successive steps, namely interval partitioning and interva mapping. The former is a many-to-one mapping which determines motion estimation performance, while the latter is a one-to-one mapping. A non-uniform quantization method was then presented to compute three initial thresholds of the interval partitioning. These initial thre-sholds were subsequently refined by using a membership function to solve the mismatch of pixel values near them caused by signal noise and so on. Afterwards, a matching criterion was discussed suitable for the motion estimation using 2 bit-depth pixels. A novel motion estimation algorithm was consequently addressed based on 2 bit-depth pixels and fuzzy quantiza-tion. To further predict the precision of the proposed algorithm, a bit resolution reduction error-motion vector precision model was built by exploiting the auto-correlation function. Extensive experimental results show that the proposed algo-rithm can always achieve high motion estimation precis rious characteristics, especially for those with detailed scene and complex motion. Compared with traditional 2 bit motion estimation, the proposed algorithm gains 0.27 dB improvement in terms of average peak signal-to-noise ratio of motion compensation.

The distribution characteristic of three-dimensional wavelet coefficients of video data was ana ed,and a scalable video coding algorithm was subsequently addressed based on hybrid three-dimensional tree and human visual system (HVS) characteristics.First,the hybrid tree structure was adaptively determined according to the auto-correlation of low-pass and high-pass coefficients.It reduced obviously the number of ion bits locating significant wavelet coefficients when scanning and processing low-pass and high-pass coefficients in temporal dimension.Second,each wavelet coefficient was weighted in terms of HVS sensitivity to its corresponding subband.Significant coefficients thus tended to be coded with high priority and arranged at the front of bitstream,and the reconstructed video quality was improved at low and medium bitrates to a great extent.Experimental results in terms of peak signal-to-noise ratio (PSNR) verified the effectiveness of the proposed algorithm on several test videos with varying characteristics.0.65dB,1.75dB,and 1.77dB higher PSNR are gained than asymmetric 3-D orientation tree for Y,U,and V components,respectively.Moreover,0.23dB,2.11dB,and 1.72dB higher PSNR are reached than single temporal-spatial orientation tree separately for Y,U,and V components.Besides,better subjective quality is obtained through effectively attenuating ringing artifact.

The distribution characteristic of three-dimensional wavelet coefficients of video data was ana ed,and a scalable video coding algorithm was subsequently addressed based on hybrid three-dimensional tree and human visual system (HVS) characteristics.First,the hybrid tree structure was adaptively determined according to the auto-correlation of low-pass and high-pass coefficients.It reduced obviously the number of ion bits locating significant wavelet coefficients when scanning and processing low-pass and high-pass coefficients in temporal dimension.Second,each wavelet coefficient was weighted in terms of HVS sensitivity to its corresponding subband.Significant coefficients thus tended to be coded with high priority and arranged at the front of bitstream,and the reconstructed video quality was improved at low and medium bitrates to a great extent.Experimental results in terms of peak signal-to-noise ratio (PSNR) verified the effectiveness of the proposed algorithm on several test videos with varying characteristics.0.65dB,1.75dB,and 1.77dB higher PSNR are gained than asymmetric 3-D orientation tree for Y,U,and V components,respectively.Moreover,0.23dB,2.11dB,and 1.72dB higher PSNR are reached than single temporal-spatial orientation tree separately for Y,U,and V components.Besides,better subjective quality is obtained through effectively attenuating ringing artifact.