In order to provide attractive content, it is important to design and manage services based on users' Quality of Experience (QoE). In recent years, researchers in the field have been increasingly focusing on the biological information approach. In this paper, we examine the relationship between the change in the hemodynamic response of blood in the frontal lobe of the brain and the preference of images by using Near-Infrared Spectroscopy (NIRS). We first measured the change in the hemodynamic response of blood in the frontal lobe by NIRS while images were shown to subjects, then performed a subjective assessment test through questionnaires. We found that Channel six in the right side frontal area is highly relevant to human preferences of images. We argue that NIRS can be effectively applied in the field of image quality assessment.

Knowing the road surface condition is one of the most important topics at contributes to the safety of pedestrians and car drivers. It is increasingly becoming necessary to distinguish road surface condition at night-time, as most of the previously methods only focus on daytime conditions. In this paper, we propose a distinction method of road surface conditions by using only video information acquired from a visible surveillance video camera. The main feature of this method is to simply use passing car's headlights as the lighting source. As a result, we were able to distinguish road surface conditions such as dry or wet with high accuracy.

Surveillance with intelligent camera systems is widely used in traffic control, driving assistance and surgery. One of the most essential specifications for surveillance systems is compactness. Therefore, in these applications with large field of view, we can avoid the use of multiple camera systems or mechanically controlled cameras. It is well known that omnidirectional camera systems using panoramic annular lens (PAL) have many advantages. Nevertheless, because of low spatial resolution of angle of elevation for the vertical direction of captured image by using PAL, the image after panoramic expansion usually has a disadvantage such as image quality degradation. To overcome this disadvantage, high quality panoramic image generation method is essential. Consequently, we propose the high quality panoramic image generation method using multiple PAL images. By synthesizing multiple PAL images for different zoom ratio, we can obtain fine quality panoramic image that improve image quality deterioration.

An effective method for detecting road surface conditions is the use of polarization features. In this paper we describe this method, for which we use two cameras for taking vertical and horizontal polarized images simultaneously. Generally, camera calibrations have to be processed, however in this paper we explain how we managed to avoid this processing, by introducing the omni-directional polarization image capture method, using an omni-directional camera and a polarization filter. This method enables to take vertical and horizontal polarized images at the same time, without using the camera calibration.

The perceived image distortion of any image is strongly depend on the local features, such as edge, flat and texture. A new objective no-reference (NR) image quality evaluation model based on local features and segmentation for JPEG coded image is presented in our previous paper, which is easy to calculate and applicable to various image processing applications. But the algorithmic thresholds investigation of the segmentation algorithm were not sufficient in the paper. Therefore in this paper, we want to investigate the suitable threshold values of our segmentation algorithm both for training and test images by the optimization method. Our experiments on various image distortion types indicate that its performs significantly better than the conventional model.

We derive a linear correspondence between the variables of an encoder and those of a corresponding syndrome former. Using the derived correspondence, we show that the log-likelihood ratio of an information bit conditioned on a received sequence can be equally calculated using the syndrome trellis. It is shown that the proposed method also applies to recursive systematic convolutional codes which are typical constituent codes for turbo codes. Moreover, we show that soft-in syndrome decoding considering a priori probabilities of information bits is possible in the same way as for Viterbi decoding based on the code trellis. Hence, the proposed method can be applied to iterative decoding such as turbo decoding. We also show that the proposed method is effective for high-rate codes by making use of trellis modification.