Image Quality Feedback-Based Adaptive Video Definition Improvement for the Space Manipulation Task

Abstract

A novel video definition enhancement technique for space manipulation task is presented in this paper. In contrast to other techniques, we implement our algorithm by considering both the feedback of the image quality (IQ) and the correction of the visual fatigue factor simultaneously. First, when designing the objective IQ evaluation metrics, we employ both the subjective evaluation result of the IQ and the information of the attention region to improve their performances. The canonical correlation analysis technique is used to build the connection between the subjective evaluation results and the objective ones. The eye tracking system is employed to search the location of the visual attention. Second, a denoising and enhancement technique in wavelet domain is developed to improve the imaging definition. The whole calculation process is monitored by the feedback of the objective IQ evaluation metrics mentioned above. The evaluation result of IQ is also corrected by some visual fatigue factors since the space tasks are always a long-time mission. The face recognition technique will be included in our system in the future to detect and analyze the visual fatigue, although it is not developed currently. Extensive experimental results have shown the effectivity of our proposed technique.

Full-Reference Image Quality Assessment Based on Spatial-Domain Combined with Frequency-Domain Analysis

“…Full-Reference IQA calculates the error between the test image and the reference image to assess the quality of the test image [3] . On this basis, Li Guoqing et al proposed a multi-level perception decomposition method for full-reference image quality assessment, which firstly used a pre-trained deep network to perform multi-level semantic decomposition on the image to obtain similarity between feature maps of different levels, and finally obtained high-level semantic image quality scores [4] . Zhisen Tang et al extracted various features in the spatial and frequency domains and used random forest algorithm for fusion, which effectively improved the accuracy of the image assessment.…”

Intelligent Luminance Control of Lighting Systems Based on Imaging Sensor Feedback

“…Improper lighting will decrease working efficiency [ 5 ], affect task scheduling, or even cause some accidents. For example, in our past research work it can be found that the visual fatigue and the human error rate may be increased when an astronaut participates in some aerospace flight tasks under improper environment lighting [ 6 ]. Figure 1 shows different lighting effects of a display, where: (a) uses one light source; (b) and (c) use two light sources, and (c) suffers from the glare.…”