Abstract: In various examples, disclosed techniques use a banding detector neural network to identify the locations and sizes of banding artifacts in pixel regions of an input image. The neural network generates a band size map that identifies at least one banding artifact in the input image. The band size map can include a set of predicted band size values, each of which corresponds to a respective pixel of the input image and represents a distance between edges of a banding artifact. The band size map and the input image are provided as input to a stochastic bilateral blur filter, which generates a de-banded image by applying blurring effects to the input image at the band locations indicated by the band size map. An inverse tone mapping operation is then performed to convert the de-banded image to an image that does not have banding artifacts.

Abstract: The technology disclosed herein involves using a machine learning model (e.g., CNN) to expand lower dynamic-range image content (e.g., SDR images) into higher dynamic-range image content (e.g., HDR images). The machine learning model can take as input the lower dynamic-range image and can output multiple expansion maps that are used to make the expanded image appear more natural. The expansion maps may be used by image operators to smooth color banding and to dim overexposed regions or user interface elements in the expanded image. The expanded content (e.g., HDR image content) may then be provided to one or more devices for display or storage.

Abstract: High dynamic range (HDR) support is provided for legacy application programs, such as games that are configured to display standard dynamic range (SDR) frames. HDR frames may be synthesized without modifying the legacy application program. The buffer creation process of the legacy application program is intercepted and modified before creation of the SDR format buffer so that the buffer is configured to use an HDR format. A location of an intermediate buffer storing HDR rendered data is determined by intercepting and analyzing graphics driver calls in a command stream produced by the legacy application program. The HDR rendered data is combined with user interface content extracted from the SDR frames. Additionally, any post processing effects used by the legacy application program to produce the SDR frames may be predicted and applied to the HDR rendered data to synthesize the HDR frames for display on a modern HDR display device.

Abstract: The technology disclosed herein involves using a machine learning model (e.g., CNN) to expand lower dynamic-range image content (e.g., SDR images) into higher dynamic-range image content (e.g., HDR images). The machine learning model can take as input the lower dynamic-range image and can output multiple expansion maps that are used to make the expanded image appear more natural. The expansion maps may be used by image operators to smooth color banding and to dim overexposed regions or user interface elements in the expanded image. The expanded content (e.g., HDR image content) may then be provided to one or more devices for display or storage.

Abstract: High dynamic range (HDR) support is provided for legacy application programs, such as games that are configured to display standard dynamic range (SDR) frames. HDR frames may be generated without modifying the legacy application program. The buffer creation process of the legacy application program is intercepted and modified before creation of the SDR format buffer so that the buffer is configured to use an upgraded SDR format having an increased bit depth compared with a conventional SDR buffer. Rather than tone mapping and quantizing rendered image data to the lower bit depth for storage in the conventional SDR buffer, the rendered image data is tone mapped and quantized for storage at the increased bit depth of the upgraded SDR buffer. Therefore, the luminance and greater dynamic range of the tone mapped data is better preserved compared with outputting conventional SDR frames.

Abstract: High dynamic range (HDR) support is provided for legacy application programs, such as games that are configured to display standard dynamic range (SDR) frames. HDR frames may be synthesized without modifying the legacy application program. The buffer creation process of the legacy application program is intercepted and modified before creation of the SDR format buffer so that the buffer is configured to use an HDR format. A location of an intermediate buffer storing HDR rendered data is determined by intercepting and analyzing graphics driver calls in a command stream produced by the legacy application program. The HDR rendered data is combined with user interface content extracted from the SDR frames. Additionally, any post processing effects used by the legacy application program to produce the SDR frames may be predicted and applied to the HDR rendered data to synthesize the HDR frames for display on a modern HDR display device.