Abstract-An improved Dijkstra shortest path algorithm is presented in this paper. The improved algorithm introduces a constraint function with weighted value to solve the defects of the data structure storage, such as lots of redundancy of space and time. The number of search nodes is reduced by ignoring reversed nodes and the weighted value is flexibly changed to adapt to different network complexity. The simulation experiment results show that the number of nodes on search shortest path and computation time is significantly reduced. Thus, improved algorithm can be faster to search out the target nodes.

In view of the fact that the current high efficiency video coding standard does not consider the characteristics of human vision, this paper proposes a perceptual video coding algorithm based on the just noticeable distortion model (JND). The adjusted JND model is combined into the transformation quantization process in high efficiency video coding (HEVC) to remove more visual redundancy and maintain compatibility. First of all, we design the JND model based on pixel domain and transform domain respectively, and the pixel domain model can give the JND threshold more intuitively on the pixel. The transform domain model introduces the contrast sensitive function into the model, making the threshold estimation more precise. Secondly, the proposed JND model is embedded in the HEVC video coding framework. For the transformation skip mode (TSM) in HEVC, we adopt the existing pixel domain called nonlinear additively model (NAMM). For the non-transformation skip mode (non-TSM) in HEVC, we use transform domain JND model to further reduce visual redundancy. The simulation results show that in the case of the same visual subjective quality, the algorithm can save more bitrates.

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It is very important to use the convolutional neural network model for urban short-term traffic congestion forecasting. However, pooling operation process is prone to cause data structure information loss when using CNN model to predict short-term traffic flow. Complete data feature information cannot be transmitted, which reduces the prediction ability of the model. To solve these problems, an innovative model based on dilated convolutional-dense networks is proposed in this paper. Firstly, the model can use the dilated convolution to obtain the characteristics of the larger receptive field with fewer network parameters, and fully extract the complex and variable data features. Then, through up-sampling and densely connected, the problem of parameter degradation in the process of increasing layers of neural network can be solved. Finally, the actual urban road average speed data blocks are taken to verify the validity of the model. Experimental results show that compared with traditional CNN model, the average absolute error of the network prediction structure is reduced by 3% to 23%.