Abstract: A method of encoding a non-linear, color space video signal includes converting the non-linear video signal to a linear video signal, transforming the non-linear signal to produce a transformed non-linear signal, transforming the linear signal to product a transformed linear signal, using the transformed linear signal to determine errors, applying the errors to the non-linear, color space video signal to produce a compensated non-linear color space signal, and encoding the compensated signal.

Abstract: A method of estimating motion in video display data includes dividing a frame of the video display data into blocks, generating at least two motion models for a current block from motion models of neighboring blocks to the current block, and selecting one of the motion models based upon a similarity of the motion model that minimizes differences between corresponding blocks in reference frames to the current block.

Abstract: A method of producing video data includes receiving, at a processor, a current frame of image data in a stream of frames of image data, dividing a current frame of image data into blocks, identifying a current block and defining a neighborhood of blocks for the current block, generating at least one initial motion vector for each block, using the initial motion vector for current block and an initial motion model to calculate a weight for each initial motion vector in the neighborhood based on a difference between initial motion vector for the current block and the initial motion vector for at least one other block from the current block in the neighborhood and differences in the image data between the current block and the other blocks in the neighborhood, using the weights for each initial motion vector to generate coefficients for a refined motion model, refining the initial motion vector for the current block according to the refined motion model to produce a refined motion vector, using the refined motio

Abstract: A method of adjusting motion in a video sequence, includes performing motion estimation on an original video sequence and producing initial motion vectors and motion statistics, using the motion statistics to determine a level of motion estimation and motion compensation (MEMC) in the video sequence, adjusting a frame interpolation process based upon the level, and producing a frame interpolated video sequence from the original video sequence and the adjusted frame interpolation process.

Abstract: A method of displaying variable rate video data includes receiving a signal from a display indicating that the display is ready to accept a frame of video data, analyzing image content of scenes in the video data, using the image content of scenes in the video data to cluster the scenes into scene types based upon a motion level, adjusting motion estimation and motion compensation to produce a variable output frame rate of the video data not synchronized with an input frame rate, the variable output frame rate based upon the scene type, wherein the variable output frame rate employs interpolated frames, wherein interpolated phases used to form the interpolated frames are determined by the motion level and a phase relationship between the input frames and the display timing, receiving the video data at the variable output frame rate at a display panel, and displaying the video data at the variable output frame rate.

Abstract: A method of performing motion vector correction in a sequence of video frames includes receiving, at a processor, a frame of video frames at a received rate lower than an original frame rate, identifying motion vector candidates for a frame in the sequence of video frames, detecting a cadence of the sequence of video frames using the motion vector candidates, scaling the motion vector candidates according to the cadence to produce scaled motion vector candidates, calculating motion vectors for a frame in the sequence of video frames using the scaled motion vector candidates, and interpolating at least one new frame of video data using the motion vectors.

Abstract: A method of converting three dimensional image data into two dimensional image data, includes identifying at least two vertices of an object to be rendered in a frame of three dimensional image data, calculating a three-dimensional (3D) motion vector for each vertex of the object to be rendered, determining a position of each vertex in a new frame, calculating the motion vectors for a block based upon the vertex position in the new frame and the motion vectors for the vertex, and using the motion vectors for the vertex to render pixels in the new frame.

Abstract: A system can include a memory and a motion vector calculator (MVC) configured to receive a block from the memory, the MVC including an MVC core configured to generate a partial motion vector (MV) for the block, a halo reduction block configured to perform halo reduction on the partial MV, and an MV post-processing block configured to smooth the partial MV and output the smoothed partial MV.

Abstract: A method includes receiving, at a processor, a frame of input image data, generating at least one motion vector field between a first frame of input image data and an adjacent frame of input image data, refining the at least one motion vector field to provide refined motion vectors, temporally filtering a frame of input image data to produce a temporally filtered frame of image data, and generating a frame of interpolated image data using the temporally filtered frame of image data and the refined motion vectors.

Abstract: A method of developing an image training library includes receiving, at a processor, a set of high resolution image samples at a first resolution, generating a set of low resolution image samples having a second resolution from the set of high resolution images, wherein the second resolution is lower than the first resolution, clustering the low resolution image samples using features in the low resolution images, generating a low resolution dictionary for each cluster, generating sparse coefficients for each sample, and using the sparse coefficients to generate a high resolution dictionary for each cluster.

Abstract: A method of scaling image data, includes receiving image data at a processor in at least two channels, the image data having a first resolution for the a channel and a second, lower resolution for other channels, using known pixel values of the first resolution pixels to generate an interpolation function to be applied to second resolution pixels co-located with a subset of the first resolution pixels, and applying the interpolation function to the second resolution pixels to find the unknown pixel values.

Abstract: A system comprises a phase plane correlation (PPC) processing module configured to receive video data from a video source and generate a motion vector (MV) candidate, and a three-dimensional (3D) recursive processing module configured to receive the MV candidate from the PPC processing module, perform 3D recursive processing on a number of MV candidates including the MV candidate received from the PPC processing module, and select one of the MV candidates based on the 3D recursive processing.

Abstract: A method of compensating for match errors while generating a super-resolution image, includes receiving, at a processor, an input image having a first resolution forming a low resolution layer, identifying a patch in a unknown high resolution image layer, identifying a corresponding low resolution patch in the low resolution layer, finding at least one matched patch in a match layer having a resolution lower than the first resolution, identifying at least one copy patch in a copy layer having a resolution lower than the high resolution layer, the copy patch to be copied to the high resolution layer, determining an match error between the at least one matched patch and a corresponding low resolution patch, selecting a matrix to transform the match error to provide compensation for the high resolution patch as match error compensation, and modifying the high resolution patch using the match error compensation.

Abstract: A method of processing video data includes upscaling an input low resolution image to a high resolution image using a processor, detecting monotonicity in a direction normal to an edge in a low resolution neighborhood in the input low resolution image, and correcting pixels in a high resolution neighborhood of the high resolution image corresponding to the low resolution neighborhood to preserve at least some of the monotonicity.

Abstract: A method of generating motion vectors for image data includes identifying boundaries of at least one object in original frames of image data, performing object motion analysis based upon the boundaries, performing pixel-level motion layer generation, using the object motion analysis and the pixel-level motion layers to generate motion for blocks in the image data, and producing block level motion information and layer information for the original frames of image data.

Abstract: A method of determining an output frame rate includes receiving an input sequence of frames of image data at an input frame rate, performing motion vector calculations on the frames of image data to produce motion vectors and motion statistics, determining a motion level in the frames using the motion statistics, and interpolating frames of image data at the output frame rate, wherein the output frame rate is based upon the motion level.

Abstract: An on-screen display (OSD) processing system can include video data fetch modules to receive video data from a video source, edge detection modules to detect an edge of a region within a scene represented by the video data, edge processing modules to process results from the edge detection modules, and an OSD confidence level adjustment module to adjust an OSD confidence level corresponding to the region within the scene.

Abstract: To denoise video data, a temporal IIR filter is applied to video data. The temporally filtered frame is used as a guide to spatially filter the video data without introducing temporal artifacts. The spatially filtered frame can then be temporally filtered again using a temporal IIR filter. The results of the second temporal filtering can then be used as a guide to eliminate noise that the spatial filter did not eliminate.

Abstract: A method to produce a frame of video data includes receiving original frame true motion information, original frame object boundary information, and original frame layer information for at least two original frames of video data, and generating interpolation phase block-level motion and interpolation phase pixel-level layer information from the original frame block-level true motion information, original frame pixel-level object boundary information, and original frame pixel-level layer information for the original frame.

Abstract: A method of reducing artifacts in video image data includes determining pixels in a block of image data that are mosquito pixels, estimating a level of mosquito noise in the block, and filtering out the mosquito noise to produce noise-filtered data.