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US 12,272,039 B2Granted
Efficient user-defined SDR-to-HDR conversion with model templates
AssigneeDolby Laboratories Licensing Corporation
InventorsGuan-Ming Su, Harshad Kadu
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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.
Claims14
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.
2 more elements
A method comprising:
- decoding, from a video signal, a standard dynamic range (SDR) image to be backward reshaped into a corresponding mapped high dynamic range (HDR) image;
- decoding, from the video signal, composer metadata that is used to derive one or more operational parameter values of content-user-specific backward reshaping mappings;
- wherein the one or more operational parameter values of content-user-specific backward reshaping mappings are predicted by one or more content-creation-user-specific modified backward reshaping metadata prediction models;
- wherein the one or more content-creation-user-specific modified backward reshaping metadata prediction models are generated based on a model template and content creation user input;
- wherein the model template includes 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 SDR images in a plurality of training image pairs and ground truth derived with a plurality of corresponding training 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,
- 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,
- wherein content creation user input modifies the plurality of corresponding training HDR images into one or more content-creation-user-adjusted HDR appearances;
- using the one or more operational parameter values of the content-user-specific backward reshaping mappings to backward reshape the SDR image into the mapped HDR image of at least one of the one or more content-creation-user-adjusted HDR appearances;
- and
- causing a display image derived from the mapped HDR image to be rendered with a display device.
7 more elements
Dependencies are inferred from the claim text.