基于结构相似度的稀疏编码模型

Sparse Coding Model Based on Structural Similarity

已有的稀疏编码模型采用误差的平方和作为信息保持的客观评价标准,但最近的研究表明,人眼视觉系统的主要功能是从视觉区域提取图像和视频中的结构化信息.引入结构相似度来衡量信息保持的程度,通过对改进的目标函数进行优化,获得与初级视皮层中具有局部性、朝向性和带通性的感受野相类似的基函数集.实验结果表明,改进后的稀疏编码模型更符合人眼视觉系统特性.

Current existing sparse coding models employ the mean square of the error between the actual image and the reconstructed image to measure how well the code describes the image. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene or a video, an alternative measure for information preservation assessment, based on the structural similarity, is introduced. After minimizing the cost function, the improved model attains a complete family of localized, oriented, and bandpass receptive fields, similar to those found in the primary visual cortex. The experimental results show that the improved sparse coding model is more consistent in human visual system.