Abstract: A system and method for encoding. In some embodiments, the method includes: encoding a first pixel of an image using a first codec; selecting, based on a remaining bit budget, a second codec; encoding a second pixel, immediately following the first pixel, using the second codec, wherein: the first codec has a first loss, according to a measure of loss; and the second codec has a second loss, according to the measure of loss, the second loss being greater than zero and less than the first loss.

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 method for processing a source code file comprises scanning the source code file to identify text lines, and analyzing, via one or more processors, the text lines with a classifier to identify one or more of the text lines that correspond to code construct type information. The code construct type information includes license information. The classifier is trained with sample source code files. The method further comprises generating a subset of the text lines that excludes the one or more of the text lines identified as corresponding to the code construct type information. Further, the method comprises determining first text lines within the subset that correspond to open source code by comparing the subset to a database. The database includes a plurality of text lines associated with open source code.

Abstract: A first reshaping mapping is performed on a first image represented in a first domain to generate a second image represented in a second domain. The first domain is of a first dynamic range different from a second dynamic range of which the second domain is. A second reshaping mapping is performed on the second image represented in the second domain to generate a third image represented in the first domain. The third image is perceptually different from the first image in at least one of: global contrast, global saturation, local contrast, local saturation, etc. A display image is derived from the third image and rendered on a display device.

Abstract: A system and a method are disclosed for encoding data for transmission, including determining a rank of a first obtained symbol of the plurality of symbols, encoding, at an encoder, the rank of the first symbol, generating a new frequency entry for the first obtained symbol by incrementing an initial histogram frequency entry of the first obtained symbol, determining, based on the new frequency entry of the first obtained symbol, that the rank of the first obtained symbol of the plurality of symbols has a constraint violation with a rank of a first violating symbol in the first encoder LUT, swapping the rank of the first obtained symbol and the rank of the first violating symbol in the first encoder LUT so the constraint violation is resolved, and generating a compressed bit-stream by iteratively applying an encoding function to each symbol of the plurality of symbols.

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: An input image of a first bit depth in an input domain is received. Forward reshaping operations are performed on the input image to generate a forward reshaped image of a second bit depth in a reshaping domain. An image container containing image data derived from the forward reshaped image is encoded into an output video signal of the second bit depth.

Abstract: A first predictor is applied to an input image to generate first-stage predicted codewords approximating prediction target codewords of a prediction target image. Second-stage prediction target values are created by performing an inverse cascade operation on the prediction target codewords and the first-stage predicted codewords. A second predictor is applied to the input image to generate second-stage predicted values approximating the second-stage prediction target values. Multiple sets of cascade prediction coefficients are generated to comprise first and second sets of cascade prediction coefficients specifying the first and second predictors. The multiple sets of cascade prediction coefficients are encoded, in a video signal, as image metadata. The video signal is further encoded with the input image.

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. When a parent scene of a secondary scene is processed by two or more neighboring nodes, initial forward reshaping functions and trim-pass correction parameters are adjusted using reference tone-mapping functions and updated scene-based trim-pass correction parameters.

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: Given input HDR and SDR images representing the same scene, a prediction model to predict the HDR image from a compressed representation of the input SDR image is generated as follows: a) generate noise data based at least on the characteristics of the HDR image b) generate a noisy SDR image by adding the noise data to the SDR image c) generate an augmented HDR data set and an augmented SDR data set by using the input HDR and SDR images and the noisy SDR image d) generate a prediction model to predict the augmented HDR data set based on the augmented SDR data set and e) solve the prediction model according to a minimization-error criterion to generate a set of prediction parameters to be transmitted to a decoder together with a compressed representation of the input SDR image to reconstruct an approximation of the input HDR image.

Abstract: In an encoder, a high-dynamic range (HDR) image is encoded using a family of local forward reshaping functions selected according to an array of forward mapping indices (FMI) indicating which local forward reshaping function needs to be used for each pixel in the HDR image to generate a reshaped standard dynamic range (SDR) image. A decoder, given the reshaped SDR image, iteratively generates a reconstructed HDR image and estimated reshaped SDR images by adjusting a local FMI array and a local array of backward mapping indices (BMI) until an error metric related to the difference between the local BMI and FMI arrays and the difference between the estimate SDR images and the reshaped SDR image satisfy a convergence criterion. Techniques for generating families of local forward reshaping functions and local backward reshaping functions based on a global forward reshaping function are also presented.

Abstract: Apparatus and methods for providing software and hardware based solutions to the problem of synthesizing noise for a digital image. According to one aspect, a probability image is generated and noise blocks are randomly placed at locations in the probability image where the locations have probability values that are compared to a threshold criterion, creating a synthesized noise image. Embodiments include generating synthesized film grain images and synthesized digital camera noise images.

Abstract: Given an input image in a high dynamic range (HDR) which is mapped to a second image in a second dynamic range using a reshaping function, to improve coding efficiency, a reshaping function generator may adjust the codeword range of the HDR input under certain criteria, such as for noisy HDR images with a relatively-small codeword range. An example of generating a scaler for adjusting the HDR codeword range based on the original codeword range and a metric of the percentage of edge-points in the HDR image is provided. The adjusted reshaping function allows for more efficient rate control during the compression of reshaped images.

Abstract: A first predictor is applied to an input image to generate first-stage predicted codewords approximating prediction target codewords of a prediction target image. Second-stage prediction target values are created by performing an inverse cascade operation on the prediction target codewords and the first-stage predicted codewords. A second predictor is applied to the input image to generate second-stage predicted values approximating the second-stage prediction target values. Multiple sets of cascade prediction coefficients are generated to comprise first and second sets of cascade prediction coefficients specifying the first and second predictors. The multiple sets of cascade prediction coefficients are encoded, in a video signal, as image metadata. The video signal is further encoded with the input image.

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: Backward reshaping metadata prediction models are trained with training SDR images and corresponding training HDR images. Content creation user input to define user adjusted HDR appearances for the corresponding training HDR images is received. Content-creation-user-specific modified backward reshaping metadata prediction models are generated based on the trained prediction models and the content creation user input. The content-creation-user-specific modified prediction models are used to predict operational parameter values of content-creation-user-specific backward reshaping mappings for backward reshaping SDR images into mapped HDR images of at least one content-creation-user-adjusted HDR appearance.

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.