Iterative optimization of reshaping functions in single-layer HDR image codec

    公开(公告)号:US12149753B2

    公开(公告)日:2024-11-19

    申请号:US17920391

    申请日:2021-04-21

    Abstract: A method, for generating (a) a forward reshaping function for compressing an input high-dynamic range (HDR) image into a reshaped standard-dynamic-range (SDR) image and (b) a backward reshaping function for decompressing the reshaped SDR image into a reconstructed HDR image, includes (i) optimizing the forward reshaping function to minimize a deviation between the reshaped SDR image and an input SDR image corresponding to the input HDR image, (ii) optimizing the backward reshaping function to minimize a deviation between the reconstructed HDR image and the input HDR image, and (iii) until a termination condition is met, applying a correction to the input SDR image and reiterating, based on the input SDR image as corrected, the steps of optimizing the forward and backward reshaping functions.

    Joint forward and backward neural network optimization in image processing

    公开(公告)号:US12003746B2

    公开(公告)日:2024-06-04

    申请号:US17800886

    申请日:2021-02-17

    Inventor: Guan-Ming Su

    CPC classification number: H04N19/436 H04N19/119 H04N19/136 H04N19/186

    Abstract: A source color grade and a destination color grade may be received by an end-to-end image mapping pipeline comprising forward and backward paths. Forward neural networks in the forward path may be applied to generate, from the source color grade, a forward reshaped color grade corresponding to the destination color grade. Backward neural networks in the backward path may be applied to generate, from the forward reshaped color grade, a backward reshaped color grade corresponding to the source color grade. An overall neural network cost function may be specified for the forward path and the backward path to generate a cost that may be used as a basis for determining operational parameters for the forward and backward neural networks.

    USER-GUIDED IMAGE SEGMENTATION METHODS AND PRODUCTS

    公开(公告)号:US20230005243A1

    公开(公告)日:2023-01-05

    申请号:US17780279

    申请日:2020-12-02

    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.

    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.

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