Trainable visual quality metrics for measuring rendering quality in a graphics environment

A method comprising:

• receiving, by circuitry of a set of processing resources providing a trained visual quality neural network, a sampled signal for visual quality measurement, wherein the sampled signal comprises a reference image of current frame of a video, and wherein the reference image comprising an original image prior to a reconstruction process being applied to the original image;
• generating, by the circuitry providing the trained visual quality neural network, a prediction of a visual quality score for the reconstructed image corresponding to the sampled signal;
• and
• notifying, by the circuitry providing the trained visual quality neural network, a visual quality measurement system of the prediction of the visual quality score for the reconstructed image.

A system comprising:

• a memory device;
• and
• a graphics processor coupled with the memory device, the graphics processor comprising a set of processing resources to perform a supersampling anti-aliasing operation, the set of processing resources including circuitry configured to:
• receive, by a trained visual quality neural network, a sampled signal for visual quality measurement, wherein the sampled signal comprises a reference image of current frame of a video, and wherein the reference image comprising an original image prior to a reconstruction process being applied to the original image;
• generate, by the trained visual quality neural network, a prediction of a visual quality score for the reconstructed image corresponding to the sampled signal;
• and
• notify, by the trained visual quality neural network, a visual quality measurement system of the prediction of the visual quality score for the reconstructed image.

A non-transitory computer-readable storage medium having stored thereon executable computer program instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:

• receiving, by circuitry of a set of processing resources providing a trained visual quality neural network, a sampled signal for visual quality measurement, wherein the sampled signal comprises a reference image of current frame of a video, and wherein the reference image comprising an original image prior to a reconstruction process being applied to the original image;
• generating, by the circuitry providing the trained visual quality neural network, a prediction of a visual quality score for the reconstructed image corresponding to the sampled signal;
• and
• notifying, by the circuitry providing the trained visual quality neural network, a visual quality measurement system of the prediction of the visual quality score for the reconstructed image.