Concerning the excessively high energy consumption, long transmission delay and poor data integrity of nodes in Wireless Sensor Network (WSN),a routing algorithm named MSTSDI (Multi-Sink Time Sensitive Data Integrity) based on multi-mobile sinks considering Quality of Service (QoS) was proposed. Firstly, The density of the nodes was determined by the strength of the signal received from the base station,and the WSN was divided into autonomous areas according to the K-means theory. Secondly, a mobile sink was assigned to each autonomous area, and the trajectory of the mobile sink was determined by using Support Vector Regression (SVR). Finally, the depth and queue potential fields were introduced to transmit data packets with high sensitivity and high data integrity through Improved-IDDR (Integrity and Delay Differentiated Routing) algorithm. Theoretical analysis and simulation results showed that compared with GLRM (Grid-based Load-balanced Routing Method) algorithm and LEACH (Low Energy Adaptive Clustering Hierarchy protocol) algorithm, the energy consumption of routing strategy improved-IDDR was decreased by 21.2% and 23.7%; and the end-to-end delay of the algorithm was decreased by 15.23% and 17.93%; the data integrity was better. Experimental results showed that MSTSDI can effectively improve the performance of the system in real networks.

The Color-to-Gray (C2G) image quality evaluation algorithm based on structural similarity does not make full use of the gradient feature of the image, and the contrast similarity feature ignores the consistency of the continuous color blocks of the image, thus leading to a large difference between the algorithm and the subjective judgment of human vision. A C2G image quality evaluation algorithm named C2G Visual Similarity Index Measurement (C2G-VSIM) was proposed based on Human Visual System (HVS). In this algorithm, the color image was regarded as the reference image, the corresponding decolorization image obtained by different algorithms was regarded as the test image. By applying color space conversion and Gaussian filtering to these reference and test images, taking full account of the characteristics of image brightness similarity and structual similarity, a new color consistency contrast feature was introduced to help C2G-VSIM to capture the global color contrast feature; then the gradient amplitude feature was also introduced into C2G-VSIM to improve the sensitivity of the algorithm to the image gradient feature. Finally, by combining those above features, a new imgage quality evaluation operator named C2G-VSIM was obtained. Experimental results on Cadík's dataset showed that in terms of accuracy and preference evaluation, the Spearman Rank Order Correlation Coefficient (SROCC) between C2G-VSIM and subjective assessment of human visuality was 0.8155 and 0.7634, respectively, the accuracy was improved significantly without increasing the time consuming compared to C2G Structure Similarity Index Measurement (C2G-SSIM). The proposed algorithm has high consistency compared to human visuality, as well as simple calculation, which can effectively and automatically evaluate decolorization images in actual project with large scale.