Abstract: Methods, apparatus, and computer-readable storage media for tone mapping High Dynamic Range (HDR) images. An input HDR image is separated into luminance and color. Luminance is processed to obtain a base layer and a detail layer. The base layer is compressed according to a non-linear remapping function to reduce the dynamic range, and the detail layer is adjusted. The layers are combined to generate output luminance, and the output luminance and color are combined to generate an output image. A base layer compression technique may be used that analyzes the details and compresses the base layer accordingly to provide space at the top of the intensity scale where the details are displayed to thus generate output images that are visually better than images generated using conventional techniques. User interface elements may be provided via which a user may control one or more parameters of the tone mapping method.

Abstract: Techniques are disclosed relating to modifying an automatically predicted adjustment. In one embodiment, the automatically predicted adjustment may be adjusted, for example, based on a rule. The automatically predicted adjustment may be based on a machine learning prediction. A new image may be globally adjusted based on the modified automatically predicted adjustment.

Abstract: Techniques are disclosed relating to automatically adjusting images. In one embodiment, an image may be automatically adjusted based on a regression model trained with a database of raw and adjusted images. In one embodiment, an image may be automatically adjusted based on a model trained by both a database of raw and adjusted images and a small set of images adjusted by a different user. In one embodiment, an image may be automatically adjusted based on a model trained by a database of raw and adjusted images and predicted differences between a user's adjustment to a small set of images and a predicted adjustment based on the database of raw and adjusted images.

Abstract: Techniques are disclosed relating to automatically adjusting images. In one embodiment, an image may be automatically adjusted based on a regression model trained with a database of raw and adjusted images. In one embodiment, an image may be automatically adjusted based on a model trained by both a database of raw and adjusted images and a small set of images adjusted by a different user. In one embodiment, an image may be automatically adjusted based on a model trained by a database of raw and adjusted images and predicted differences between a user's adjustment to a small set of images and a predicted adjustment based on the database of raw and adjusted images.

Abstract: Methods, apparatus, and computer-readable storage media for tone mapping High Dynamic Range (HDR) images. An input HDR image is separated into luminance and color. Luminance is processed to obtain a base layer and a detail layer. The base layer is compressed according to a non-linear remapping function to reduce the dynamic range, and the detail layer is adjusted. The layers are combined to generate output luminance, and the output luminance and color are combined to generate an output image. A base layer compression technique may be used that analyzes the details and compresses the base layer accordingly to provide space at the top of the intensity scale where the details are displayed to thus generate output images that are visually better than images generated using conventional techniques. User interface elements may be provided via which a user may control one or more parameters of the tone mapping method.

Abstract: Techniques are disclosed relating to generating generic labels, translating generic labels to image pipeline-specific labels, and automatically adjusting images. In one embodiment, generic labels may be generated. Generic algorithm parameters may be generated based on training a regression algorithm with the generic labels. The generic labels may be translated to pipeline-specific labels, which may be usable to automatically adjust an image.

Abstract: Techniques are disclosed relating to automatically adjusting images. In one embodiment, an image may be automatically adjusted based on a regression model trained with a database of raw and adjusted images. In one embodiment, an image may be automatically adjusted based on a model trained by both a database of raw and adjusted images and a small set of images adjusted by a different user. In one embodiment, an image may be automatically adjusted based on a model trained by a database of raw and adjusted images and predicted differences between a user's adjustment to a small set of images and a predicted adjustment based on the database of raw and adjusted images.

Abstract: Techniques are disclosed relating to automatically adjusting images. In one embodiment, an image may be automatically adjusted based on a regression model trained with a database of raw and adjusted images. In one embodiment, an image may be automatically adjusted based on a model trained by both a database of raw and adjusted images and a small set of images adjusted by a different user. In one embodiment, an image may be automatically adjusted based on a model trained by a database of raw and adjusted images and predicted differences between a user's adjustment to a small set of images and a predicted adjustment based on the database of raw and adjusted images.

Abstract: Techniques are disclosed relating to modifying an automatically predicted adjustment. In one embodiment, the automatically predicted adjustment may be adjusted, for example, based on a rule. The automatically predicted adjustment may be based on a machine learning prediction. A new image may be globally adjusted based on the modified automatically predicted adjustment.

Abstract: Techniques are disclosed relating to generating generic labels, translating generic labels to image pipeline-specific labels, and automatically adjusting images. In one embodiment, generic labels may be generated. Generic algorithm parameters may be generated based on training a regression algorithm with the generic labels. The generic labels may be translated to pipeline-specific labels, which may be usable to automatically adjust an image.