natural scene statistics

NSS Models for Detecting Picture Artifacts

Netflix and other video content providers are tasked with delivering top-notch video quality to hundreds of millions of subscribers. As these providers continue to increase the sizes of their collection, a substantial percentage of the acquired video content will contain visual artifacts produced at the time of the video's production. These artifacts can include de-interlacing errors, up-sampling distortions, and other annoying visual defects that could greatly reduce the perceptual quality and ultimately the quality of experience of the subscriber/viewer.

Perceptual Fog Density Assessment and Image Defogging

Prof. Alan Bovik and his student Lark Kwon Choi in the WNCG Laboratory for Image and Video Engineering (LIVE) have developed a no-reference perceptual fog density prediction model and a perceptual image defogging algorithm based on natural scene statistics (NSS) and fog aware statistical features.

Improving Infrared Image Quality

While much work has been done to further image quality for cameras and smart phones in the visible light spectrum, WNCG student Todd Goodall and his advisor Prof. Bovik have expanded their research to include the quality of infrared images.

“As far as Prof. Bovik and I know, no one has thoroughly studied the natural scene statistics of infrared images,” Goodall states. “Other general image statistics have been studied, but no one has considered the perceptually-relevant natural statistics..”

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