Exposure Stacks of Live Scenes with Hand-held Cameras

Abstract. Many computational photography applications require the user to take multiple pictures of the same scene with different camera settings. While this allows to capture more information about the scene than what is possible with a single image, the approach is limited by the requirement that the images be perfectly registered. In a typical scenario the camera is hand-held and is therefore prone to moving during the capture of an image burst, while the scene is likely to contain moving objects. Combining such images without careful registration introduces annoying artifacts in the final image. This paper presents a method to register exposure stacks in the presence of both camera motion and scene changes. Our approach warps and modifies the content of the images in the stack to match that of a reference image. Even in the presence of large, highly non-rigid displacements we show that the images are correctly registered to the reference.

Repairing imperfect video enhancement algorithms using classification-based trained filters

There are numerous video processing algorithms and modules available. When the algorithms are not optimally tuned, undesired results may happen in the processed video signals, e.g. blurring, overshoots/downshoots, loss of details and aliasing. When the video processing modules are fixed, e.g. when the modules are implemented in hardware/chips, it is highly desirable to repair those unpleasant effects caused by certain imperfect algorithms. In this paper, we propose a solution based on classification and least squares trained filters to repair/patch low-quality video processing modules at the back end of a video chain. Extensive experiments show that the repairing method can significantly improve the video quality without modifying the original processing modules

Quality Assessment in Computer Graphics

International audienceIn this chapter, we review the existing works regarding visual quality assessment in computer graphics. This broad area of research includes many sub-domains which make intensive use of quality assessment and/or artifact visibility evaluation: geometry processing, rendering, HDR imaging, tone-mapping, and stereo vision. For each of these sub-domains, we present the existing objective quality metrics, the subjective quality experiments as well as an evaluation and comparison of their performance. We broadly classify these existing works into image-based (i.e., evaluating artifacts introduced in 2D rendered images and videos) and model-based approaches (i.e., artifacts introduced on the 3D models themselves). Finally, the last part presents the emerging trends and main future directions