In standard digital color imaging, each pixel position acquires data for only one color plane and the remaining two color planes must be inferred through a process known as demosaicking. Furthermore, the image is susceptible to blurring artifacts due to a moving camera or fast moving subject. In this work we develop a robust framework to demosaick the color filter array (CFA) image while reducing the blur corrupting the image. We begin by defining a color motion blur model that describes the motion blur artifacts affecting color images. We then integrate the motion blur model in the demosaicking algorithm to obtain a computationally efficient framework for deblurring while demosaicking.

Due to the sample-and-hold nature of liquid crystal display (LCD) image formation, LCDs suffer from motion picture blur. This is especially evident during scenes containing fast motion due to the inherent sample-and-hold nature of LCD image formation. Using models for the human visual system (HVS) we take a signal processing approach to solving this problem by pre-processing the data before it is sent to the display. Whereas previous pre-processing approaches either apply a simple high pass filter or an iterative deconvolution algorithm, this work uses a small collection of efficient linear FIR filters to reduce the amount of perceived motion blur. Specifically, we develop a two-channel non-perfect reconstruction filter bank to reduce the motion dependent low pass effects of the HVS. Perceptual tests indicate that our algorithm reduces the amount of perceived motion blur on LCDs at a lower complexity than the existing deconvolution approach.

Liquid crystal displays (LCDs) have shown great promise in the consumer market for their use as both computer and television displays. Despite their many advantages, the inherent sample-and-hold nature of LCD image formation results in a phenomenon known as motion blur. In this work, we develop a method for motion blur reduction using the Richardson-Lucy deconvolution algorithm in concert with motion vector information from the scene. We further refine our approach by introducing a perceptual significance metric that allows us to weight the amount of processing performed on different regions in the image. In addition, we analyze the role of motion vector errors in the quality of our resulting image. Perceptual tests indicate that our algorithm reduces the amount of perceivable motion blur in LCDs.

Modern video compression codecs achieve high compression efficiency by exploiting the temporal and spatial redundancies present in video sequences. Although state of the nit block based intra and inter prediction have been refined to better adapt to the content in the scene, they still exhibit limitations in fully extracting information from the decoded data. We propose the addition of an in-loop prediction refinement stage that is initialized with the conventional intra or inter predicted result and is capable of further reducing the spatial redundancies present in the sequence. By extracting additional information from the decoded neighborhood, the refinement stage is able to bring the prediction closer to the original data, thus improving compression efficiency. In this work, we study one possible method of refinement for the particular case of intra prediction. This uses sparse decomposition of blocks that overlap decoded neighboring blocks and the current predicted block in order to make prediction more coherent with already encoded data.

Liquid crystal displays (LCD) have shown great promise in the consumer market for their use as both computer and television displays. Despite their many advantages, the inherent nature of LCD image formation results in a phenomenon known as motion blur. LCDs emit light and aim to hold it constant during the entire frame time. In this paper we develop a method for motion blur reduction using the Richardson-Lucy deconvolution algorithm in concert with motion vector information from the scene. In addition, we derive a general lower bound on the performance of the deconvolution algorithm. Perceptual tests indicate that our algorithm is indeed effective at reducing the amount of motion blur on LCDs.