The search functionality is under construction.
The search functionality is under construction.

Author Search Result

[Author] Xiping WANG(2hit)

1-2hit
  • Performance Improvement by Using an Edge-Adaptive Estimation Algorithm in FMSN-VQ

    Xiping WANG  Shinji OZAWA  

     
    LETTER-Signals,Circuits and Images

      Vol:
    E74-A No:5
      Page(s):
    1023-1027

    This letter proposes an edge-adaptive estimation algorithm for estimating the mean and standard deviation of the image block in the Mean-Separated and Normalized Vector Quantizer with Feedback Estimation (FMSN-VQ). The adaptation is performed according to the block state which is estimated from its neighboring coded blocks with the consideration of the edge presence and orientation. The simulation results show that the estimation error is significantly reduced and higher SNR is achieved compared to FMSN-VQ with a fixed estimator. Furthermore, this algorithm adds very little complexity.

  • A Mean-Separated and Normalized Vector Quantizer with Edge-Adaptive Feedback Estimation and Variable Bit Rates

    Xiping WANG  Shinji OZAWA  

     
    PAPER-Image Processing, Computer Graphics and Pattern Recognition

      Vol:
    E75-D No:3
      Page(s):
    342-351

    This paper proposes a Mean-Separated and Normalized Vector Quantizer with edge-Adaptive Feedback estimation and variable bit rates (AFMSN-VQ). The basic idea of the AFMSN-VQ is to estimate the statistical parameters of each coding block from its previous coded blocks and then use the estimated parameters to normalize the coding block prior to vector quantization. The edge-adaptive feedback estimator utilizes the interblock correlations of edge connectivity and gray level continuity to accurately estimate the mean and standard deviation of the coding block. The rate-variable VQ is to diminish distortion nonuniformity among image blocks of different activities and to improve the reconstruction quality of edges and contours to which the human vision is sensitive. Simulation results show that up to 2.7dB SNR gain of the AFMSN-VQ over the non-adaptive FMSN-VQ and up to 2.2dB over the 1616 ADCT can be achieved at 0.2-1.0 bit/pixel. Furthermore, the AFMSN-VQ shows a comparable coding performance to ADCT-VQ and A-PE-VQ.