Intersection capacity based on driver's visual characteristics

In order to reflect the influence of the drivers＇ characteristic differences on intersection capacity under a mixed traffic flow, a driver correction coefficient for the intersection capacity calculation according to the driver＇s visual characteristics is proposed. First, the parameters of the driver＇s visual characteristics at some real roads, including gaze fixation distribution, mean fixation duration, visual angle distribution and some other parameters at intersections, are collected. Then, the relationship between the traffic flow rate at intersections and the parameters of driver eye movements are established. The analytical results indicate that when the traffic flow is unsaturated, the parameters of driver eye movements change relatively little; however, when the traffic flow is saturated, the parameters of driver eye movements change drastically. Finally, the saturation-flow-rate model is modified according to the parameters of driver eye movements; thus, a capacity model of intersections considering the driver＇s visual characteristics is obtained.

Adaptive image enhancement algorithm based on fuzzy entropy and human visual characteristics

To overcome the shortcomings of the Lee image enhancement algorithm and its improvement based on the logarithmic image processing(LIP) model, this paper proposes what we believe to be an effective image enhancement algorithm. This algorithm introduces fuzzy entropy, makes full use of neighborhood information, fuzzy information and human visual characteristics.To enhance an image, this paper first carries out the reasonable fuzzy-3 partition of its histogram into the dark region, intermediate region and bright region. It then extracts the statistical characteristics of the three regions and adaptively selects the parameter αaccording to the statistical characteristics of the image’s gray-scale values. It also adds a useful nonlinear transform, thus increasing the ubiquity of the algorithm. Finally, the causes for the gray-scale value overcorrection that occurs in the traditional image enhancement algorithms are analyzed and their solutions are proposed.The simulation results show that our image enhancement algorithm can effectively suppress the noise of an image, enhance its contrast and visual effect, sharpen its edge and adjust its dynamic range.

Based on the visual communication design under the influence of the digital media research

The visual communication design is mainly ~om the perspective of people, heard with the help of media technology to convey the information to the design form of object. Visual communication design on people＇s behavior and lifestyle has obvious effect, due to the widespread attention of people. The development of modem social economy and science and technology has changed the way of information extraction, requires designers must be under the influence of digital media technology with more diverse forms of design of visual communication design research. In this paper, on the basis of the brief analysis of the visual communication characteristics and under the influence of digital media design language features, and studies the influence of digital media under the trend of the development of visual communication design several opinions are put forward.

A FINE GRANULAR JOINT SOURCE CHANNEL CODING METHOD

An improved FGS (Fine Granular Scalability) coding method is proposed in this letter, which is based on human visual characteristics. This method adjusts FGS coding frame rate according to the evaluation of video sequences so as to improve the coding efficiency and subject perceived quality of reconstructed images. Finally, a fine granular joint source channel coding is proposed based on the source coding method, which not only utilizes the network resources efficiently, but guarantees the reliable transmission of video information.

A HIGH SPEED ADAPTIVE FRACTAL IMAGE CODING ALGORITHM USING FLEXIBLE CLASSIFICATION TECHNIQUE

Traditionally, fractal image compression suffers from lengthy encoding time in measure ofhours. In this paper, combined with characteristlcs of human visual system, a flexible classification technique is proposed. This yields a corresponding adaptive algorithm which can cut down the encoding timeinto second's magnitude. Experiment results suggest that the algorithm can balance the overall encodingperformance efficiently, that is, with a higher speed and a better PSNR gain.