Dolby Laboratories Licensing Corporation

Neeraj J Gadgil

Guan-Ming Su

Harshad Kadu

Qing Song

Tsung-Wei Huang

Yoon Yung Lee

Amirhossein Khalilian-gourtani

Systems and methods for context-based encoding of video data using reshaping algorithms. One method includes receiving the video data, the video data composed of a plurality of image frames, each image frame including a plurality of pixel blocks. The method includes determining, for each pixel block, a luma bin index, determining, for each luma bin, a banding risk value, and determining Gaussian function parameters based on the banding risk value. The method includes generating a differential reshaping function using the Gaussian function parameters, computing a luma-based forward reshaping function based on the differential reshaping function, and generating an output image for each image frame by applying the luma-based forward reshaping function to the respective image frame.

Context-based reshaping algorithms for encoding video data

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.

User-guided image segmentation methods and products

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.

Adaptive streaming with false contouring alleviation

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.

Adaptive local reshaping for SDR-to-HDR up-conversion

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.

Recursive segment to scene segmentation for cloud-based coding of HDR video

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.

Tensor-product b-spline predictor

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.

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Pune, India

Neeraj J Gadgil