Blind/Referenceless Image Spatial Quality Evaluator

We propose a natural scene statistic based Blind/Referenceless Image Spatial QUality Evaluator (BRISQUE) which extracts the point wise statistics of local normalized luminance sig-nals and measures image naturalness (or lack there of) based on measured deviations from a natural image model. We also model the distribution of pairwise statistics of adjacent normalized luminance signals which provides distortion ori-entation information. Although multi scale, the model uses easy to compute features making it computationally fast and time efficient. The frame work is shown to perform statisti-cally better than other proposed no reference algorithms and full reference structural similarity index (SSIM). 1

Real-time quality assessment of videos from body-worn cameras

Videos captured with body-worn cameras may be affected by distortions such as motion blur, overexposure and reduced contrast. Automated video quality assessment is therefore important prior to auto-tagging, event or object recognition, or automated editing. In this paper, we present M-BRISQUE, a spatial quality evaluator that combines, in real-time, the Michelson contrast with features from the Blind/Referenceless Image Spatial QUality Evaluator. To link the resulting quality score to human judgement, we train a Support Vector Regressor with Radial Basis Function kernel on the Computational and Subjective Image Quality database. We show an example of application of M-BRISQUE in automatic editing of multi-camera content using relative view quality, and validate its predictive performance with a subjective evaluation and two public datasets

Low contrast detection factor based contrast enhancement and restoration for underwater images

7-13Marine ecosystem is the largest of earth’s aquatic ecosystems. It includes salt marshes, coral reefs, deep sea, sea floor, etc. To learn deep about the activities taking place inside, underwater imaging is a tool. But these images lack in contrast and brightness leading to the lack of information in the ocean activities. To enhance such low contrast underwater images, Low Contrast Detection Factor (LCDF) is proposed in this study. It uses the value, saturation and hue to enhance the low contrast regions and to restore the color. Quality assessment is done to substantiate the proposed algorithm. It is found that the entropy gives an average of 7.3. No-reference Quality Metrics such as Natural Image Quality Evaluator and Blind/ Referenceless Image Spatial Quality Evaluator shows an average value of 3.6 and 22.5, respectively. The blur metrics shows a value of 0.21. The quality metrics indicates that the naturalness of the underwater image is maintained while the contrast of the underwater image has increased

Low contrast detection factor based contrast enhancement and restoration for underwater images

Marine ecosystem is the largest of earth’s aquatic ecosystems. It includes salt marshes, coral reefs, deep sea, sea floor, etc. To learn deep about the activities taking place inside, underwater imaging is a tool. But these images lack in contrast and brightness leading to the lack of information in the ocean activities. To enhance such low contrast underwater images, Low Contrast Detection Factor (LCDF) is proposed in this study. It uses the value, saturation and hue to enhance the low contrast regions and to restore the color.  Quality assessment is done to substantiate the proposed algorithm. It is found that the entropy gives an average of 7.3. No-reference Quality Metrics such as Natural Image Quality Evaluator and Blind/ Referenceless Image Spatial Quality Evaluator shows an average value of 3.6 and 22.5, respectively. The blur metrics shows a value of 0.21. The quality metrics indicates that the naturalness of the underwater image is maintained while the contrast of the underwater image has increased

Evaluation of Blur and Gaussian Noise Degradation in Images Using Statistical Model of Natural Scene and Perceptual Image Quality Measure

In this paper we present new method for classification of image degradation type based on Riesz transform coefficients and Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) that employs spatial coefficients. In our method we use additional statistical parameters that gives us statistically better results for blur and all tested degradations together in comparison with previous method. A new method to determine level of blur and Gaussian noise degradation in images using statistical model of natural scene is presented. We defined parameters for evaluation of level of Gaussian noise and blur degradation in images. In real world applications reference image is usually not available therefore proposed method enables classification of image degradation by type and estimation of Gaussian noise and blur levels for any degraded image