US 12,272,039 B2
Efficient user-defined SDR-to-HDR conversion with model templates
Guan-Ming Su, Fremont, CA (US); and Harshad Kadu, Santa Clara, CA (US)
Assigned to DOLBY LABORATORIES LICENSING CORPORATION, San Francisco, CA (US)
Appl. No. 17/635,684
Filed by DOLBY LABORATORIES LICENSING CORPORATION, San Francisco, CA (US)
PCT Filed Aug. 12, 2020, PCT No. PCT/US2020/046032
§ 371(c)(1), (2) Date Feb. 15, 2022,
PCT Pub. No. WO2021/030506, PCT Pub. Date Feb. 18, 2021.
Claims priority of provisional application 62/887,123, filed on Aug. 15, 2019.
Claims priority of application No. 19191921 (EP), filed on Aug. 15, 2019.
Prior Publication US 2022/0301124 A1, Sep. 22, 2022
Int. Cl. G06T 5/92 (2024.01)
CPC G06T 5/92 (2024.01) [G06T 2207/10024 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20208 (2013.01)] 14 Claims
OG exemplary drawing
 
1. A method comprising:
accessing a model template comprising backward reshaping metadata prediction models, wherein the backward reshaping metadata prediction models are trained with a plurality of training image feature vectors from a plurality of training standard dynamic range (SDR) images in a plurality of training image pairs and ground truth derived with a plurality of corresponding training high dynamic range (HDR) images in the plurality of training image pairs, wherein each training image pair in the plurality of training image pairs comprises a training SDR image in the plurality of training SDR images and a corresponding training HDR image in the plurality of corresponding training HDR images, wherein the training SDR image and the corresponding training HDR image in each such training image pair depict same visual content but with different luminance dynamic ranges;
receiving content creation user input that defines one or more content-creation-user-adjusted HDR appearances for the plurality of corresponding training HDR images;
generating, based on the model template and the content creation user input, content-creation-user-specific modified backward reshaping metadata prediction models; and
using the content-creation-user-specific modified backward reshaping metadata prediction models 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 of the one or more content-creation-user-adjusted HDR appearances, wherein the backward reshaping metadata prediction models comprise a plurality of Gaussian process regression (GPR) models for predicting a luminance backward reshaping mapping to backward reshape input luminance SDR codewords into mapped luminance HDR codewords, and the content creation user input modifies a plurality of sample points of the luminance backward reshaping mapping.