Interpolation of reshaping functions

    公开(公告)号:US11388408B2

    公开(公告)日:2022-07-12

    申请号:US17299743

    申请日:2019-11-27

    Abstract: Methods and systems for generating an interpolated reshaping function for the efficient coding of high-dynamic range images are provided. The interpolated reshaping function is constructed based on a set of pre-computed basis reshaping functions. Interpolation schemes are derived for pre-computed basis reshaping functions represented as look-up tables, multi-segment polynomials, or matrices of coefficients in a multivariate, multi-regression representation. Encoders and decoders using asymmetric reshaping and interpolated reshaping functions for mobile applications are also presented.

    Reducing banding artifacts in backward-compatible HDR imaging

    公开(公告)号:US11277646B2

    公开(公告)日:2022-03-15

    申请号:US17282523

    申请日:2019-10-02

    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.

    TENSOR-PRODUCT B-SPLINE PREDICTOR
    17.
    发明申请

    公开(公告)号:US20220408081A1

    公开(公告)日:2022-12-22

    申请号:US17764394

    申请日:2020-09-29

    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.

    HDR image representations using neural network mappings

    公开(公告)号:US11361506B2

    公开(公告)日:2022-06-14

    申请号:US17045941

    申请日:2019-04-08

    Abstract: Methods and systems for mapping images from a first dynamic range to a second dynamic range using a set of reference color-graded images and neural networks are described. Given a first and a second image representing the same scene but at a different dynamic range, a neural network (NN) model is selected from a variety of NN models to determine an output image which approximates the second image based on the first image and the second image. The parameters of the selected NN model are derived according to an optimizing criterion, the first image and the second image, wherein the parameters include node weights and/or node biases for nodes in the layers of the selected NN model. Example HDR to SDR mappings using global-mapping and local-mapping representations are provided.

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

    公开(公告)号:US10701404B2

    公开(公告)日:2020-06-30

    申请号:US16329392

    申请日:2017-08-28

    Abstract: 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.

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