最后提出了一种改进的评价方法——视觉加权信噪比模型(VWSNR)。
Finally, an improved algorithm of image quality assessment is presented, which is Vision and Weighted Signal-to-Noise Ratio (VWSNR).
进行矢量量化前，首先对小波系数进行视觉加权，然后采用了同向跨频带的方法将小波系数进行矢量组织。
Before quantifying, the wavelet coefficients should be given a vision weight, and then organized in the way of across different subbands according to the same direction.
基于人眼视觉特性的加权因子改变编码位序而不是编码值，该算法有更快的编码速度以及较好的压缩效果。
The human visual weighting factor change the order of coding rather than the value of coding. The algorithm has much faster coding speed and better compression effect.
然后通过对基元特征进行加权投影统计，得到图像的方向性、对比度等纹理特征，这些特征可以更好的适应人类视觉特性。
The texture features such as direction and contrast of the image can be obtained from weighted projection statistics of primitive feature, which are more matched with human vision.
匹配方向方面，提出了加权视觉色差公式，并设计了实用的三维搜索匹配模型。
For mapping direction, the weighted formula of visual color difference is proposed and the three-dimensional mapping model is designed.
该方法保留了小波的视觉效果，同时在很大程度上减少了运算的复杂度，优化了加权系数的计算方法，得到了较好的融合效果。
These methods maintained the visual effect of wavelet, reduced the computation complexity to a great extent, and optimized the computation of weighted coefficients.
充分利用人眼视觉掩盖效应，针对不同的失真类型对于图像不同的内容具有不同的质量影响，提出了一种新的加权策略。
Aiming at different quality effects on different image region and distortion type, we take full advantage of the visual masking effect and proposed a new weighted idea.
不同的特征对视觉显著性的贡献是不同的，为此提出一种能够自动进行特征选择和加权的图像显著区域检测方法。
Different features have different contribution to saliency, so a new approach for salient region detection with automatic feature selection and weighting is proposed.
算法首先根据视觉注意理论提取时域和空域特征，并建立加权混合模型。
Firstly, the algorithm constructs a weighted combination model based on spatial-temporal features by using information theory.
算法首先根据视觉注意理论提取时域和空域特征，并建立加权混合模型。
Firstly, the algorithm constructs a weighted combination model based on spatial-temporal features by using information theory.
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