基于纹理分解的变换域JND模型及图像编码方法

为了提高变换域JND模型的精度，在计算对比度掩盖因子时只对纹理分量滤波并判断区域类型的方法避免了JND低估的问题。将改进的JND模型用于图像编码，考虑到辅助信息对编码效率的影响，把经过调整后的JND模型结合到量化过程中，能去除更多的视觉冗余并保持兼容性。仿真结果表明，纹理分解的方法提高了JND阈值，改进的编码方法在相似的视觉质量下能节省更多的码率并且不需要增加额外的比特开销，该编码思路也适用于视频编码。

Improved sub-band JND model with textural decomposition andits application in perceptual image coding

In order to improve the accuracy of the just noticeable difference (JND) model in transform domain, an enhanced JND model with a new method for contrast masking factor estimation is proposed. The image was decomposed and the textural image is used for an accurate block classification, thus the accurate JND in DCT domain is obtained. The improved JND model was applied on the perceptual image coding. Considering the compatibility and the auxiliary information which will affect the encoding efficiency, the JND model was adjusted to the quantization process and removed more visual redundancy. Experimental results show that the proposed algorithm can improve the JND threshold; compared with JPEG standard, the perceptual coding method can save more bit rate and does not need extra bit for auxiliary information at the similar visual quality. The proposed algorithm is also applicable to the perceptual video coding.