Dolby Laboratories Licensing Corporation

Vid Scale, Inc.

Dolby International Ab

Meta Platforms, Inc.

Interdigital Vc Holdings, Inc.

Thomson Licensing Dtv

Qian Chen

Guan-Ming Su

Peng Yin

Yan Ye

Jie Dong

Sheng Qu

Tao Chen

Qing Song

Robin Atkins

Harshad Kadu

Hubert Koepfer

Scott J Daly

Yunfei Zheng

Shankar Lakshmi Regunathan

Xiaoan Lu

Joel Sole

Samir N Hulyalkar

Bongsun Lee

Neeraj J Gadgil

Minchuan Chen

David Brooks

Yee Jin Lee

Ralf Willenbacher

Systems, methods, and instrumentalities are disclosed relating to intra prediction of a video signal based on mode-dependent subsampling. A block of coefficients associated with a first sub block of a video block, one or more blocks of coefficients associated with one or more remaining sub blocks of the video block, and an indication of a prediction mode for the video block may be received. One or more interpolating techniques, a predicted first sub block, and the predicted sub blocks of the one or more remaining sub blocks may be determined. A reconstructed first sub block and one or more reconstructed remaining sub blocks may be generated. A reconstructed video block may be formed based on the prediction mode, the reconstructed first sub block, and the one or more reconstructed remaining sub blocks.

Systems and methods for spatial prediction

Systems, methods, and non-transitory computer-readable media can receive a video uploaded by a user. A determination is made as to whether the video satisfies passthrough eligibility criteria. An ABR video package associated with the video is generated based on the determining whether the video satisfies the passthrough eligibility criteria.

Systems and methods for providing passthrough adaptive bitrate videos

In some embodiments, an encoder device is disclosed to generate single-channel standard dynamic range/high dynamic range content predictors. The device receives a standard dynamic range image content and a representation of a high dynamic range image content. The device determines a first mapping function to map the standard dynamic range image content to the high dynamic range image content. The device generates a single channel prediction metadata based on the first mapping function, such that a decoder device can subsequently render a predicted high dynamic range image content by applying the metadata to transform the standard dynamic range image content to the predicted high definition image content.

Single-channel inverse mapping for image/video processing

Given a standard-dynamic range (SDR) video input, techniques for generating and compressing composer metadata describing inverse luma and chroma reshaping functions are described. Given the SDR input, the composer metadata allow a decoder to generate a corresponding output in high-dynamic range. Three techniques are proposed: a static, sequence-based, architecture, a two-stage, scene-based, distributed solution with a centralized post-processing method, and a single-stage distributed solution using overlapped segments. Techniques to reduce the amount of transmitted composer metadata are also described.

Segment-based reshaping for coding high dynamic range video

For each content-mapped frame of a scene, it is determined whether the content mapped frame is susceptible to object fragmentation with respect to texture in a homogeneous region based on statistical values derived from the content-mapped image and a source image mapped into the content-mapped image. The homogeneous region is a region of consistent texture in the source image. Based on a count of content-mapped frames susceptible to object fragmentation in homogeneous region, it is determined whether the scene is susceptible to object fragmentation in homogeneous region. If so, an upper limit for mapped codewords for a prediction function for predicting codewords of a predicted image from the mapped codewords in the content-mapped image is adjusted. Mapped codewords above the upper limit are clipped to the upper limit.

Preserving texture/noise consistency in video codecs

Real-time forward reshaping, comprising selecting a statistical sliding window that indexes with the current frame, having also, a look-back frame and a look-ahead frame, determining whether they are part of the current scene, determining a noise parameter, a luma transfer function and a luma forward reshaping function based on the luma transfer function and the noise parameter within the current scene, selecting a central tendency sliding window of the current frame and the look-back frame within the current scene, and determining a central tendency luma forward reshaping function. The chroma reshaping comprises analyzing statistics for the extended dynamic range (EDR) weights and EDR upper bounds, mapping these to standard dynamic range (SDR) weights and SDR upper bounds based on the central tendency luma forward reshaping function, determining a chroma content-dependent polynomial and a central tendency chroma forward reshaping polynomial and generating chroma MMR coefficients.

Real-time reshaping of single-layer backwards-compatible codec

Methods and systems for adaptive chroma reshaping are discussed. Given an input image, a luma-reshaped image is first generated based on its luma component. For each chroma component of the input image, the range of the pixel values in the luma reshaped image is divided into bins, and for each bin a maximal scale factor is generated based on the chroma pixel values in the input image corresponding to the pixels of the luma reshaped image in the bin. A forward reshaping function is generated based on a reference reshaped function and the maximal scale factors, and reshaped chroma pixel values for the chroma component are generated based on the forward reshaping function and the corresponding pixel values in the luma reshaped image. Implementations options using look-up tables for mobile platforms with limited computational resources are also described.

Chroma reshaping for high dynamic range images

Given HDR and SDR video inputs representing the same content, segment-based methods are described to generate a backward-compatible reshaped SDR video which preserves the artistic intent or 'look' of the inputs and satisfies other coding requirements. For each frame in a segment, reshaping functions are generated based on a support frames set determined based on a sliding window of frames that is adjusted based on scene cuts in the segment and which may include frames from both the current segment and neighboring segments. For luma reshaping, a mapping that preserves the cumulative density function of the luminance histogram values in the EDR and SDR inputs is combined with a minimum codeword allocation derived based on the EDR signal and the support frame set. For chroma reshaping, methods for segment-based forward and backward reshaping using multivariate, multi-regression models are also presented.

Coding of high dynamic range video using segment-based reshaping

A processor for video coding receives a full-frame rate (FFR) HDR video signal and a corresponding FFR SDR video signal. An encoder generates a scalable bitstream that allows decoders to generate half-frame-rate (HFR) SDR, FFR SDR, HFR HDR, or FFR HDR signals. Given odd and even frames of the input FFR SDR signal, the scalable bitstream combines a base layer of coded even SDR frames with an enhancement layer of coded packed frames, where each packed frame includes a downscaled odd SDR frame, a downscaled even HDR residual frame, and a downscaled odd HDR residual frame. In an alternative implementation, the scalable bitstream combines four signals layers: a base layer of even SDR frames, an enhancement layer of odd SDR frames, a base layer of even HDR residual frames and an enhancement layer of odd HDR residual frames. Corresponding decoder architectures are also presented.

Backward-compatible HDR codecs with temporal scalability

Pixel data of a video sequence with enhanced dynamic range (EDR) are predicted based on pixel data of a corresponding video sequence with standard dynamic range (SDR) and an inter-layer predictor. Under a highlights clipping constrain, conventional SDR to EDR prediction is adjusted as follows: a) given a highlights threshold, the SDR to EDR predictor is adjusted to output a fixed output value for all input SDR pixel values larger than the highlights threshold, and b) given a dark-regions threshold, the residual values between the input EDR signal and its predicted value are set to zero for all input SDR pixel values lower than the dark-regions threshold. Example processes to determine the highlights and dark-regions thresholds and whether highlights clipping is occurring are provided.

Inter-layer prediction for signals with enhanced dynamic range

In a decoder, a processor extracts a control map of false contour filtering from a part of a multi-layer video signal that includes a low dynamic range image mapped from an original high-dynamic range (HDR) image. It determines one or more filter parameters for a sparse finite-impulse-response (FIR) filter, where the one or more filter parameters relate to at least in part on the control map of false contour filtering and a predicted image predicted from the low dynamic range image. It applies the sparse FIR filter to filter pixel values in a portion of the predicted image based at least in part on the control map of false contour filtering, and it reconstructs a version of the original HDR image based at least in part on the portion of the predicted image as filtered by the FIR filter.

Hardware efficient sparse FIR filtering in layered video coding

Pixel data of a video sequence with enhanced dynamic range (EDR) are predicted based on pixel data of a corresponding video sequence with standard dynamic range (SDR) and a piecewise inter-layer predictor. The output parameters of the piecewise predictor are computed based atleast on two sets of pre-computed values and a prediction cost criterion. The first set of pre-computed values applies to all input SDR frames and comprises a set of SDR pixel values raised to one or more integer power terms. The second set of pre-computed values is frame specific and is computed based on a histogram of an input SDR frame and pixel values of the corresponding EDR frame. The pre-computed values allow for a fast iterative algorithm to identify the best pivot points for the piecewise polynomials according to a prediction cost and to solve for the coefficients of the piecewise predictor.

Piecewise inter-layer prediction for signals with enhanced dynamic range

An encoder receives an input enhanced dynamic range (EDR) image and a corresponding lower dynamic range (LDR) image to be coded at a given target rate. Before coding, a pre-dithering process is applied to the input LDR image to generate a dithered LDR image at a second bit depth, lower than its original bit depth. The pre-dithering process includes: generating uniformly-distributed noise, applying a spatial filter to the noise to generate low-pass or high-pass filtered noise, applying a temporal high pass or low pass filter to the spatially-filtered noise to generate output noise, adding the output noise to the input LDR image to generate a noise-enhanced LDR image, and quantizing the noise-enhanced image to generate the dithered LDR image. Selecting the characteristics of the dithering filters is based on both the target bit rate and luminance characteristics of the pixels in the input LDR image.

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San Jose, CA, United States of America

Qian Chen