The image quality was studied using event-related potentials (ERPs), behavioral data, and psychological measurement. In our experiment, different quality images were obtained by changing σ values in the Gaussian kernel function. The results showed that measurable ERPs, behavioral data, and psychological parameters varied with the change in the image quality. The more impaired image elicited the higher and earlier ERPs components over all scalp areas. Meanwhile, the response time reduced and detection rate increased with an increase in the blurriness level σ. The P300 and N200 components of different scalp areas elicited by different image qualities were analyzed. The N200 amplitude and P300 latency over the anterior scalp region were stronger and shorter than the ones over central and posterior scalp regions. The P300 and N200 amplitude over the middle scalp location were stronger than the ones over left and right scalp locations. The influence of image content is not significant. The image quality could be assessed by P300 and N200 amplitude over the nine scalp areas, and the anterior and middle scalp areas have higher PLCC, SROCC, and KROCC values than other scalp areas. The influence of P300 amplitude on the estimated scores is smaller than that of N200 for most scalp areas. The result had a good correlation with subjective rating scores, which proved the feasibility of utilizing ERPs to assess image quality.
A no reference video quality assessment metric based on the region of interest (ROI) was proposed in this paper. In the
metric, objective video quality was evaluated by integrating the quality of two compressed artifacts, i.e. blurring
distortion and blocking distortion. The Gaussian kernel function was used to extract the human density maps of the
H.264 coding videos from the subjective eye tracking data. An objective bottom-up ROI extraction model based on
magnitude discrepancy of discrete wavelet transform between two consecutive frames, center weighted color opponent
model, luminance contrast model and frequency saliency model based on spectral residual was built. Then only the
objective saliency maps were used to compute the objective blurring and blocking quality. The results indicate that the
objective ROI extraction metric has a higher the area under the curve (AUC) value. Comparing with the conventional
video quality assessment metrics which measured all the video quality frames, the metric proposed in this paper not only
decreased the computation complexity, but improved the correlation between subjective mean opinion score (MOS) and
objective scores.
The technology of scanning-backlight can effectively reduce motion blur in LCD, but reintroduce large area flicker
phenomenon. Perception experiments were performed to study the flicker visibility in a scanning-backlight LCD system.
Different operational modes of the scanning-backlight were used to generate different light performance. Five color
blocks: red, green, blue, white and yellow were chosen as experimental stimuli to check the influence of color on flicker
visibility in the most strict situation. Two natural images, for each with a colorful version, a black-and-white version,
together with a uniform white block (without any details) with the same average luminance of the natural image, were
adopted to verify the influence of color and details in image content on flicker visibility in normal viewing situation.
Results show that, color has no statistically significant influence on flicker visibility when luminance profiles are similar.
And details in image content can effectively decrease sensitivity to flicker visibility, which could because details can
distract viewer's attention away from flicker perception.
Artificial neural network is used in the inverse design of an electron gun's main lens in this paper. The relationship between electron spot on the screen and the structure of the main lens is investigated. According to the requirement of the spot on the screen, the structure of the main lens is investigated. According to the requirement of the spot on the screen, the structure of the main lens can be obtained through the trained network in a short time. The complex electron optic simulation can be avoided. It shows that the artificial neural network method is an effective tool to solve the inverse designing problem in the electron optic system.
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