Abstract: A set of tensor-product B-Spline (TPB) basis functions is determined. A set of selected TPB prediction parameters to be used with the set of TPB basis functions for generating predicted image data in mapped images from source image data in source images of a source color grade is generated. The set of selected TPB prediction parameters is generated by minimizing differences between the predicted image data in the mapped images and reference image data in reference images of a reference color grade. The reference images correspond to the source images and depict same visual content as depicted by the source images. The set of selected TPB prediction parameters is encoded in a video signal as a part of image metadata along with the source image data in the source images. The mapped images are caused to be reconstructed and rendered with a recipient device of the video signal.

Abstract: Methods, systems, and devices implement intra-prediction for hexagonally-sampled compression and decompression of videos and images having a regular grid of hexagonally-shaped pixels. For encoding, a prediction unit (PU) shape is selected at a sequence level from the group consisting of parallelogram, zigzag-square, hexagonal super-pixel, a rectangular zigzag and an arrow, and the hexagonally-sampled image is divided into regions based on the PU shape. For each region: a prediction mode and a PU size are determined; reference pixels are determined for each predicted pixel in the PU shape based on the prediction mode; a weighted factor is determined for each of the reference pixels based on a distance between the reference pixel and the predicted pixel; and a predicted value of each of the predicted pixels in the PU shape is determined using the corresponding reference pixels and the weighted factors.

Abstract: A device includes an electronic processor configured to define a first set of sample pixels from a set of sample pixels determined from received video data according to a first electro-optical transfer function (EOTF) in a first color representation of a first color space; convert the first set of sample pixels to a second EOTF via a mapping function, producing a second set of sample pixels according to the second EOTF; convert the first and second set of sample pixels from the first color representation to a second color representation of the first color space; determine a backward reshaping function by repeatedly applying and adjusting a sample backward reshaping function so as to minimize a difference between predicted pixel values obtained by applying the sample backward reshaping function to the pixels of the converted first set and the pixels of the converted second set.

Abstract: A global index value is generated for selecting a global reshaping function for an input image of a relatively low dynamic range using luma codewords in the input image. Image filtering is applied to the input image to generate a filtered image. The filtered values of the filtered image provide a measure of local brightness levels in the input image. Local index values are generated for selecting specific local reshaping functions for the input image using the global index value and the filtered values of the filtered image. A reshaped image of a relatively high dynamic range is generated by reshaping the input image with the specific local reshaping functions selected using the local index values.

Abstract: In a cloud-based system for encoding high dynamic range (HDR) video, each node receives a video segment and bumper frames. Each segment is subdivided into primary scenes and secondary scenes to derive scene-based forward reshaping functions that minimize the amount of reshaping-related metadata when coding the video segment, while maintaining temporal continuity among scenes processed by multiple nodes. Methods to generate scene-based forward and backward reshaping functions to optimize video coding and improve the coding efficiency of reshaping-related metadata are also examined.

Abstract: A forward reshaping mapping is generated to map a source image to a corresponding forward reshaped image of a lower dynamic range. The source image is spatially downsampled to generate a resized image into which noise is injected to generate a noise injected image. The forward reshaping mapping is applied to map the noise injected image to generate a noise embedded image of the lower dynamic range. A video signal is encoded with the noise embedded image and delivered to a recipient device for the recipient device to render a display image generated from the noise embedded image.

Abstract: In a cloud-based system for encoding high dynamic range (HDR) video, a computing node is assigned to be a dispatcher node, segmenting the input video into scenes and generating a scene to segment allocation to be used by other computing nodes. The scene to segment allocation process includes one or more iterations with an initial random assignment of scenes to computing nodes, followed by a refined assignment based on optimizing the allocation cost across all the computing nodes. Methods to generate scene-based forward and backward reshaping functions to optimize video coding and improve the coding efficiency of reshaping-related metadata are also examined.

Abstract: Methods and systems for reducing banding artifacts when displaying images are described. Identified image bands are filtered using an adaptive sparse finite response filter, where the tap-distance in the sparse filter is adapted according to an estimated width of each image band. Image debanding may be performed across multiple pixel orientations, such as rows, columns, a 45-degree angle, or a ?45-degree angle. Given a threshold to decide whether sparse filtering needs to be performed or not, an iterative debanding process is also proposed.

Abstract: A method for image segmentation includes (a) clustering, based upon k-means clustering, pixels of an image into first clusters, (b) outputting a cluster map of the first clusters (c) re-clustering the pixels into a new plurality of non-disjoint pixel-clusters, and (d) classifying the non-disjoint pixel-clusters in categories, according to a user-indicated classification. Another method for image segmentation includes (a) forming a graph with each node of the graph corresponding to a first respective non-disjoint pixel-cluster of the image and connected to each terminal of the graph and to all other nodes corresponding to other respective non-disjoint pixel-clusters that, in the image, are within a neighborhood of the first respective non-disjoint pixel-cluster, (b) setting weights of connections of the graph according to a user-indicated classification in categories respectively associated with the terminals, and (c) segmenting the image into the categories by cutting the graph based upon the weights.

Abstract: A set of tensor-product B-Spline (TPB) basis functions is determined. A set of selected TPB prediction parameters to be used with the set of TPB basis functions for generating predicted image data in mapped images from source image data in source images of a source color grade is generated. The set of selected TPB prediction parameters is generated by minimizing differences between the predicted image data in the mapped images and reference image data in reference images of a reference color grade. The reference images correspond to the source images and depict same visual content as depicted by the source images. The set of selected TPB prediction parameters is encoded in a video signal as a part of image metadata along with the source image data in the source images. The mapped images are caused to be reconstructed and rendered with a recipient device of the video signal.

Abstract: A device includes an electronic processor configured to define a first set of sample pixels from a set of sample pixels determined from received video data according to a first electro-optical transfer function (EOTF) in a first color representation of a first color space; convert the first set of sample pixels to a second EOTF via a mapping function, producing a second set of sample pixels according to the second EOTF; convert the first and second set of sample pixels from the first color representation to a second color representation of the first color space; determine a backward reshaping function by repeatedly applying and adjusting a sample backward reshaping function so as to minimize a difference between predicted pixel values obtained by applying the sample backward reshaping function to the pixels of the converted first set and the pixels of the converted second set.

Abstract: Methods and systems for image denoising when displaying high-dynamic-range images are described. Given an input image in a first dynamic range, and an input backward reshaping function mapping codewords from the first dynamic range to a second dynamic range, wherein the second dynamic range is equal or higher than the first dynamic range, statistical data based on the input image and the input backward reshaping function are generated to estimate the risk of noise artifacts in a target image in the second dynamic range generated by applying the input backward reshaping function to the input image. Using a measure of the variance in codeword bins in histograms of consecutive input frames (denoted as temporal histogram variance), a modified backward reshaping function is generated, which when applied to the input image to generate the target image eliminates or reduces noise artifacts in the target image.

Abstract: Methods and systems for reducing banding artifacts when displaying high-dynamic-range images are described. Given an input image in a first dynamic range, and an input backward reshaping function mapping codewords from the first dynamic range to a second dynamic range, wherein the second dynamic range is equal or higher than the first dynamic range, statistical data based on the input image and the input backward reshaping function are generated to estimate the risk of banding artifacts in a target image in the second dynamic range generated by applying the input backward reshaping function to the input image. Separate banding alleviation algorithms are applied in the darks and highlights parts of the first dynamic range to generate a modified backward reshaping function, which when applied to the input image to generate the target image eliminates or reduces banding in the target image.

Abstract: In some embodiments, an encoder device is disclosed to receive an input video stream containing images in a first dynamic range including a first image. The device receives a second image representing the first image. The device obtains statistical data for the first and the second images. The device determines, at a first time delay, a scene cut data from the input video stream and storing the scene cut data in a first sliding window. The device determines, at a second time delay, a first smoothing mapping function based on a second sliding window and the determined scene cut data. The device determines, at a third time delay, a second smoothing mapping function based on a third sliding window and the determined scene cut data. The device generates, at the third time delay, a composer metadata for the first image based on the first and second smoothing mapping functions.

Abstract: A standard dynamic range (SDR) image is received. Composer metadata is generated for mapping the SDR image to an enhanced dynamic range (EDR) image. The composer metadata specifies a backward reshaping mapping that is generated from SDR-EDR image pairs in a training database. The SDR-EDR image pairs comprise SDR images that do not include the SDR image and EDR images that corresponds to the SDR images. The SDR image and the composer metadata are encoded in an output SDR video signal. An EDR display operating with a receiver of the output SDR video signal is caused to render an EDR display image. The EDR display image is derived from a composed EDR image composed from the SDR image based on the composer metadata.

Abstract: Methods and systems for reducing banding artifacts when displaying high-dynamic-range images reconstructed from coded reshaped images are described. Given an input image in a high dynamic range (HDR) which is mapped to a second image in a second dynamic range, banding artifacts in a reconstructed HDR image generated using the second image are reduced by a) in darks and mid-tone regions of the input image, adding noise to the input image before being mapped to the second image, and b) in highlights regions of the input image, modifying an input backward reshaping function, wherein the modified backward reshaping function will be used by a decoder to map a decoded version of the second image to the reconstructed HDR image. An example noise generation technique using simulated film-grain noise is provided.

Abstract: Methods and systems for reducing banding artifacts when displaying images are described. Identified image bands are filtered using an adaptive sparse finite response filter, where the tap-distance in the sparse filter is adapted according to an estimated width of each image band. Image debanding may be performed across multiple pixel orientations, such as rows, columns, a 45-degree angle, or a ?45-degree angle. Given a threshold to decide whether sparse filtering needs to be performed or not, an iterative debanding process is also proposed.

Abstract: Sequence-level parameters are generated for an image frame sequence including sequence-level indicators for indicating metadata types present for each image frame in the sequence of image frames. Frame-present parameters are generated for a specific image frame in the sequence including frame-present indicators corresponding to the metadata types as indicated in the sequence-level parameters. The frame-present indicators identify first metadata types for which metadata parameter values are to be encoded in a coded bitstream as metadata payloads. The image frame sequence, the sequence-level parameters, the frame-present parameters and the metadata payloads are encoded in the coded bitstream. A recipient device can generate, from the specific image frame based partly on the metadata parameter values determined for the first metadata types, a target display image for a target display.

Abstract: Training image pairs comprising training SDR image and corresponding training HDR images are received. Each training image pair in the training image pairs comprises a training SDR image and a corresponding training HDR image. The training SDR image and the corresponding training HDR image in the training image pair depict same visual content but with different luminance dynamic ranges. Training image feature vectors are extracted from training SDR images in the training image pairs. The training image feature vectors are used to train backward reshaping metadata prediction models for predicting operational parameter values of backward reshaping mappings used to backward reshape SDR images into mapped HDR images.

Abstract: Methods and systems for reducing banding artifacts when displaying high-dynamic-range images reconstructed from coded reshaped images are described. Given an input image in a high dyna mic range (HDR) which is mapped to a second image in a second dynamic range, banding artifacts in a reconstructed HDR image generated using the second image are reduced by a) in darks and mid-tone regions of the input image, adding noise to the input image before being mapped to the second image, and b) in highlights regions of the input image, modifying an input backward reshaping function, wherein the modified backward reshaping function will be used by a decoder to map a decoded version of the second image to the reconstructed HDR image. An example noise generation technique using simulated film-grain noise is provided.