Our research is focused on examining a stereoscopic quality assessment model for stereoscopic images with disparate quality in left and right images for glasses-free stereo vision. In this paper, we examine the objective assessment model of 3-D images, considering the difference in image quality between each view-point generated by the disparity-compensated coding. A overall stereoscopic image quality can be estimated by using only predicted values of left and right 2-D image qualities based on the MPEG-7 descriptor information without using any disparity information. As a result, the stereoscopic still image quality is assessed with high prediction accuracy with correlation coefficient=0.98 and average error=0.17.

Digital video encapsulates the time series of a frame (still) images, where overall video quality can be obtained by using the quality of each frame image and the temporal information between the frame image. Coding of video produces degradation of these two types of information. These degradations can be classified as spatial degradation (static degradation) of a frame images and temporal degradation between frame image (dynamic degradation). In the framework of video quality evaluation it is necessary to consider those degradations, because their contents are strongly interdependable and quantification is problematic for these degradations. Therefore, the development of an objective video quality assessment method for single video quality requires to investigate how much static degradation and dynamic degradation affect single video quality. In this research, single video quality was predicted highly accuratly by using frame quality as static degradation and frame rate information as dynamic degradation.

In order to evaluate the vehicle detection method, it is necessary to know the correct vehicle position considered as “ground truth”. We propose indices considering subjective evaluation in vehicle detection utilizing IoU. Subjective evaluation experiments were carried out with respect to misregistration from ground truth in vehicle detection.

The social infrastructure, including roads and bridges built during period of rapid economic growth in Japan, is now aging, and there is a need to strategically maintain and renew the social infrastructure that is aging. On the other hand, road maintenance in rural areas is facing serious problems such as reduced budgets for maintenance and a shortage of engineers due to the declining birthrate and aging population. Therefore, it is difficult to visually inspect all roads in rural areas by maintenance engineers, and a system to automatically detect road damage is required. This paper reports practical improvements to the road damage model using YOLOv5, an object detection model capable of real-time operation, focusing on road image features.

Our research is focused on examining the Image Quality Assessment Model based on the MPEG-7 descriptor and the No Reference model. The model retrieves a reference image using image search and evaluate its subject score as a pseudo Reduced Reference model. The MPEG-7 descriptor was originally used for content retrieval, but we discovered that the MPEG-7 descriptor can also be used for image quality assessment. We examined the performance of the proposed model and the results revealed that this method has a higher performance rating than the SSIM.

Our research is focused on examining the video quality assessment model based on the MPEG-7 descriptor. Video quality is estimated by using several features based on the predicted frame quality such as average value, worst value, best value, standard deviation, and the predicted frame rate obtained from descriptor information. As a result, assessment of video quality can be conducted with a high prediction accuracy with correlation coefficient=0.94, standard deviation of error=0.24, maximum error=0.68 and outlier ratio=0.23.

In this paper, we consider the relationship between human preference and brain activity, especially pulse wave information using NIRS. First of all, we extracted the information of on pulse wave from the Hb changes signal of NIRS. By using the FFT to the Hb signals, we found out the 2-nd peak of power spectrum that is implying the frequency information of the pulse wave. The frequency deviation of 2-nd peak may have some information about the change of brain activity, it is associated with the human preference for viewing the significant image content.

In this paper, a novel cumulant-based adaptive notch filtering technique for the enhancement and tracking of a single sinusoid in additive noise is presented. In this technique, the enhanced signal is obtained as the output of a narrow bandpass filter implemented using a second-order pole-zero constraint IIR adaptive notch filter, which needs only one coefficient to be updated. The filter coefficient, which leads to identifying and tracking the sinusoidal frequency, is updated using a suggested adaptive algorithm employing a recursive estimate of the kurtosis and only one-sample-lag point of a selected one-dimensional fourth-order cumulant slice of the input signal. Therefore, the proposed technique provides automatically resistance to additive Gaussian noise. It is also shown that the presented technique outperforms the correlation-based counterpart in handling additive non-Gaussian noise. Simulation results are provided to show the effectiveness of the proposed algorithm in comparison with the correlation-based lattice algorithm.