Deep SDR-HDR Conversion

Abstract

The exemplary embodiments relate to converting Standard Dynamic Range (SDR) content to High Dynamic Range (HDR) content using a machine learning system. In some embodiments, the neural network is trained to convert an input SDR image into an HDR image using the encoded representation of a training SDR image and a training HDR image. In other embodiments, the neural network is trained to convert an input SDR image into an HDR image using a predefined set of color grading actions and the training images.

1 - 20 . (canceled) 
     
     
         21 : A method comprising:
 obtaining multiple training images, wherein the training images include a training standard dynamic range (SDR) image and a training high dynamic range (HDR) image;   training a neural network, using the training images and a set of color grading actions, for converting SDR images into HDR images;   receiving an input SDR image; and   converting the input SDR image into the HDR image using the training images and one or more color grading actions from the set of color grading actions.   
     
     
         22 : The method of  claim 21 , further comprising:
 receiving a user input to modify the one or more color grading actions; and   modifying the HDR image based on the user input.   
     
     
         23 : The method of  claim 21 , wherein training the neural network comprises:
 applying a first color grading action from the set of color grading actions to the training SDR image;   wherein the first color grading action is selected based on the training HDR image.   
     
     
         24 : The method of  claim 21 , wherein the neural network is configured to extract contextual features or color features from the training SDR image. 
     
     
         25 : The method of  claim 21 , wherein the set of color grading actions includes at least one of adjusting brightness, adjusting contrast, adjusting color saturation or adjusting exposure. 
     
     
         26 : A system comprising:
 one or more processors configured to:
 obtain multiple training images, wherein the training images include a training standard dynamic range (SDR) image and a training high dynamic range (HDR) image; 
 train a neural network, using the training images and a set of color grading actions, for converting SDR images into HDR images; 
 receive an input SDR image; and 
 convert the input SDR image into the HDR image using the training images and one or more color grading actions from the set of color grading actions. 
   
     
     
         27 : The system of  claim 26 , the one or more processors are further configured to:
 receive a user input to modify the one or more color grading actions; and   modify the HDR image based on the user input.   
     
     
         28 : The system of  claim 26 , wherein training the neural network comprises:
 applying a first color grading action from the set of color grading actions to the training SDR image;   wherein the first color grading action is selected based on the training HDR image.   
     
     
         29 : The system of  claim 26 , wherein the neural network is configured to extract contextual features or color features from the training SDR image. 
     
     
         30 : The system of  claim 26 , wherein the set of color grading actions includes at least one of adjusting brightness, adjusting contrast, adjusting color saturation or adjusting exposure. 
     
     
         31 : A method comprising:
 receiving a standard dynamic range (SDR) image;   converting the SDR image into a high dynamic range (HDR) image using a neural network trained to reconstruct a rolled off highlight in the HDR image that is not visible in the SDR image;   generating a set of tonal curves based on performing a regression on the HDR image;   receiving a user input to modify the set of tonal curves; and   modifying the HDR image based on the modified set of tonal curves.   
     
     
         32 : The method of  claim 31 , further comprising:
 displaying a representation of the tonal curves that includes a control point, wherein the user input includes manipulating the control point.   
     
     
         33 : The method of  claim 31 , wherein the rolled off highlight is reconstructed in a region of the HDR image that corresponds to a region of the SDR image that includes a clipped light level caused by a dynamic range of luminosity of the SDR image. 
     
     
         34 : The method of  claim 31 , wherein the SDR image uses a first dynamic range of luminosity, and the HDR image uses a second dynamic range of luminosity, and wherein converting the SDR image into to the HDR image includes mapping a first value defined relative to the first dynamic range of luminosity to a second value defined relative to the second dynamic range of luminosity. 
     
     
         35 : The method of  claim 31 , wherein the SDR image uses a first color gamut and the HDR image uses a second color gamut, and wherein converting the SDR image into to the HDR image includes mapping a first value defined relative to the first color gamut to a second value defined relative to the second color gamut. 
     
     
         36 : The method of  claim 31 , wherein converting the SDR image into the HDR image includes:
 generating a low dimensional latent representation of the SDR image; and   decoding the low dimensional latent representation of the SDR image into the HDR image.