Feature based bitrate allocation in non-backward compatible multi-layer codec via machine learning

A method of assigning bitrates to base layers and enhancement layers in a dual codec, the method comprising:

• scanning a scene of a video for bright and dark areas and accumulating feature data of the scene, and determining feature values of one or more features of the scene using the accumulated feature data of the scene;
• calculating an alpha value based on the bright and dark areas, wherein the alpha value is the exponent of a formula for forward reshaping of a luminance channel of the video;
• decomposing the scene into a base layer and at least one enhancement layer, wherein said decomposing involves determining the base layer and the at least one enhancement layer of the video based on the alpha value;
• determining a bitrate class of the base layer and the at least one enhancement layer by processing the feature values of the one or more features of the scene against a machine learning model;
• and
• setting the bitrates of the base layer and the at least one enhancement layer based on the determined bitrate class,
• wherein processing of the feature values of the one or more features against the machine learning model comprises:
• mapping the feature values of the one or more features into N dimensional space where N is the number of the one or more features;
• and
• determining where the mapped feature values is located in the N dimensional space compared to predetermined hyperplanes of the N dimensional space.