Aiming at the problem of quality evaluation of color restoration image for digital protection of faded cultural relics, the objective quality evaluation methods were researched. Combined the computational advantage of Peak Signal-to-Noise Ratio (PSNR) and structure characteristic of human visual feature information entropy, a color image quality evaluation method was proposed based on information entropy of visual features. A quality evaluation function with weights and the corresponding evaluation algorithm process were established, and the weights were determined by normalization method. Then the function value for comparing the similarity between the color restoration image and the reference color image was calculated by using the evaluation algorithm process. The smaller the value was, the higher the similarity was, and the better the corresponding color restoration image quality was, which could be used for the objective judgement of color restoration method. The quality evaluation parameters of four different performance restoration methods were compared. The experimental results show that, the evaluation results are consistent with the subjective perception of human eyes, and the proposed method is effective.

Some E-commerce sites cannot deploy independent recommender systems themselves due to limited resources. In order to help these sites deploy recommender systems quickly and conveniently, an E-commerce recommendation platform based on Software-as-a-Service (SaaS) pattern was proposed and implemented. This platform used unified scripts to collect user action information and provided recommendation services through standard interface. It realized a low coupling between platform and E-commerce sites so that the cost of implementation was reduced. The results of online operations show that this platform can help E-commerce sites increase the conversion rate and the volume of orders.