Abstract: Coding syntaxes in compliance with same or different VDR specifications may be signaled by upstream coding devices such as VDR encoders to downstream coding devices such as VDR decoders in a common vehicle in the form of RPU data units. VDR coding operations and operational parameters may be specified as sequence level, frame level, or partition level syntax elements in a coding syntax. Syntax elements in a coding syntax may be coded directly in one or more current RPU data units under a current RPU ID, predicted from other partitions/segments/ranges previously sent with the same current RPU ID, or predicted from other frame level or sequence level syntax elements previously sent with a previous RPU ID. A downstream device may perform decoding operations on multi-layered input image data based on received coding syntaxes to construct VDR images.

Abstract: Enhancing image dynamic range is described. An input video signal that is represented in a first color space with a first color gamut, which is related to a first dynamic range, is converted to a video signal that is represented in a second color space with a second color gamut. The second color space is associated with a second dynamic range. At least two (e.g., three) color-related components of the converted video signal are mapped over the second dynamic range.

Abstract: Coding syntaxes in compliance with same or different VDR specifications may be signaled by upstream coding devices such as VDR encoders to downstream coding devices such as VDR decoders in a common vehicle in the form of RPU data units. VDR coding operations and operational parameters may be specified as sequence level, frame level, or partition level syntax elements in a coding syntax. Syntax elements in a coding syntax may be coded directly in one or more current RPU data units under a current RPU ID, predicted from other partitions/segments/ranges previously sent with the same current RPU ID, or predicted from other frame level or sequence level syntax elements previously sent with a previous RPU ID. A downstream device may perform decoding operations on multi-layered input image data based on received coding syntaxes to construct VDR images.

Abstract: Novel methods and systems for image processing and gamut mapping are disclosed, where image parameters in a first color space are transformed into parameters in a second color space. In one example, the gamut mapping system processes the image parameters in a parametrized space using substantially rectangular spectral functions. In another example, the gamut mapping system processes the image parameters in a parametrized space using smooth spectral functions. The smooth spectral functions may include a linear combination of functions, including at least a cosine square function. Image parameters may include an intensity parameter, a center wavelength parameter, and a saturation parameter.

Abstract: Coding syntaxes in compliance with same or different VDR specifications may be signaled by upstream coding devices such as VDR encoders to downstream coding devices such as VDR decoders in a common vehicle in the form of RPU data units. VDR coding operations and operational parameters may be specified as sequence level, frame level, or partition level syntax elements in a coding syntax. Syntax elements in a coding syntax may be coded directly in one or more current RPU data units under a current RPU ID, predicted from other partitions/segments/ranges previously sent with the same current RPU ID, or predicted from other frame level or sequence level syntax elements previously sent with a previous RPU ID. A downstream device may perform decoding operations on multi-layered input image data based on received coding syntaxes to construct VDR images.

Abstract: Novel methods and systems for image processing and gamut mapping are disclosed, where image parameters in a first color space are transformed into parameters in a second color space. In one example, the gamut mapping system processes the image parameters in a parametrized space using substantially rectangular spectral functions. In another example, the gamut mapping system processes the image parameters in a parametrized space using smooth spectral functions. The smooth spectral functions may include a linear combination of functions, including at least a cosine square function. Image parameters may include an intensity parameter, a center wavelength parameter, and a saturation parameter.

Abstract: Enhancing image dynamic range is described. An input video signal that is represented in a first color space with a first color gamut, which is related to a first dynamic range, is converted to a video signal that is represented in a second color space with a second color gamut. The second color space is associated with a second dynamic range. At least two (e.g., three) color-related components of the converted video signal are mapped over the second dynamic range.

Abstract: Enhancing image dynamic range is described. An input video signal that is represented in a first color space with a first color gamut, which is related to a first dynamic range, is converted to a video signal that is represented in a second color space with a second color gamut. The second color space is associated with a second dynamic range. At least two (e.g., three) color-related components of the converted video signal are mapped over the second dynamic range.

Abstract: A first image stream has a first dynamic range and a first color space. First and the second image streams are received in a layered codec. The second image stream has a second dynamic range, which is higher than the first dynamic range. The first image stream is in the codec's base layer; the second image stream is in its enhancement layer. The first image stream is encoded to obtain an encoded image stream, which is decoded to obtain a decoded image stream. The decoded image stream is converted from the first non-linear or linear color space to a second, different color space to obtain a color converted image stream. A higher dynamic range image representation of the color converted image stream is generated to obtain a transformed image stream. Inverse tone mapping parameters are generated based on the transformed image stream and the second image stream.

Abstract: A substantially rectangular spectral representation is synthesized, which is adapted to produce image capture device sensor outputs if applied to an image capture device. The synthesized substantially rectangular spectral representation can be utilized in generating output color values of an output color space from image capture device sensor outputs, where the image capture device sensor outputs correspond to an image captured by an image capture device. The generated output color values correspond to colors perceived by the human visual system for the same image as that captured by the image capture device. Image capture device gamut is also determined.

Abstract: A substantially rectangular spectral representation is synthesized, which is adapted to produce either (a) image capture device sensor outputs if applied to an image capture device or (b) color values if applied to corresponding analysis functions. Spectral expansion, which can be used in various image processing methods, is achieved with the synthesized spectral representation.

Abstract: A substantially rectangular spectral representation is synthesized, which is adapted to produce either (a) image capture device sensor outputs if applied to an image capture device or (b) color values if applied to corresponding analysis functions. Spectral expansion, which can be used in various image processing methods, is achieved with the synthesized spectral representation.

Abstract: An inter-layer reference picture is generated either by considering an inverse mapped signal from a base layer, a temporal signal from an enhancement layer, or a combination of both.

Abstract: Coding syntaxes in compliance with same or different VDR specifications may be signaled by upstream coding devices such as VDR encoders to downstream coding devices such as VDR decoders in a common vehicle in the form of RPU data units. VDR coding operations and operational parameters may be specified as sequence level, frame level, or partition level syntax elements in a coding syntax. Syntax elements in a coding syntax may be coded directly in one or more current RPU data units under a current RPU ID, predicted from other partitions/segments/ranges previously sent with the same current RPU ID, or predicted from other frame level or sequence level syntax elements previously sent with a previous RPU ID. A downstream device may perform decoding operations on multi-layered input image data based on received coding syntaxes to construct VDR images.

Abstract: A substantially rectangular spectral representation is synthesized, which is adapted to produce image capture device sensor outputs if applied to an image capture device. The synthesized substantially rectangular spectral representation can be utilized in generating output color values of an output color space from image capture device sensor outputs, where the image capture device sensor outputs correspond to an image captured by an image capture device. The generated output color values correspond to colors perceived by the human visual system for the same image as that captured by the image capture device. Image capture device gamut is also determined.

Abstract: Inter-color image prediction is based on color grading modeling. Prediction is applied to the efficient coding of images and video signals of high dynamic range. Prediction models may include a color transformation matrix that models hue and saturation color changes and a non-linear function modeling color correction changes. Under the assumption that the color grading process uses a slope, offset, and power (SOP) operations, an example non linear prediction model is presented.

Abstract: Inter-color image prediction is based on color grading modeling. Prediction is applied to the efficient coding of images and video signals of high dynamic range. Prediction models may include a color transformation matrix that models hue and saturation color changes and a non-linear function modeling color correction changes. Under the assumption that the color grading process uses a slope, offset, and power (SOP) operations, an example non linear prediction model is presented.

Abstract: A first video signal is accessed, and represented in a first color space with a first color gamut, related to a first dynamic range. A second video signal is accessed, and represented in a second color space of a second color gamut, related to a second dynamic range. The first accessed video signal is converted to a video signal represented in the second color space. At least two color-related components of the converted video signal are mapped over the second dynamic range. The mapped video signal and the second accessed video signal are processed. Based on the processing, a difference is measured between the processed first and second video signals. A visual quality characteristic relates to a magnitude of the measured difference between the processed first and second video signals. The visual quality characteristic is assessed based, at least in part, on the measurement of the difference.

Abstract: Inter-color image prediction is based on color grading modeling. Prediction is applied to the efficient coding of images and video signals of high dynamic range. Prediction models may include a color transformation matrix that models hue and saturation color changes and a non-linear function modeling color correction changes. Under the assumption that the color grading process uses a slope, offset, and power (SOP) operations, an example non linear prediction model is presented.

Abstract: Given a set of two images with visual dynamic range (VDR), wherein the second image is an approximation of the first image, a standard dynamic range (SDR) equivalent peak value (EPV) is computed, wherein the EPV is smaller than the maximum pixel value of the first VDR image. An image assessment metric for the second image is computed using the EPV and an error measurement between the first image and the second image. The EPV may be computed using image statistics (e.g., mean and standard deviation) derived from the first VDR image or, if available, from its corresponding SDR image.