Abstract:
Inter-color image prediction is based on multi-channel multiple regression (MMR) models. Image prediction is applied to the efficient coding of images and video signals of high dynamic range. MMR models may include first order parameters, second order parameters, and cross-pixel parameters. MMR models using extension parameters incorporating neighbor pixel relations are also presented. Using minimum means-square error criteria, closed form solutions for the prediction parameters are presented for a variety of MMR models.
Abstract:
In an embodiment, a media source combines reference code values and mapping function parameters for mapping functions into video frames originally designated to carry pixel values. The video frames are delivered to a downstream device such as a media sink in an encoded video signal. The media sink extracts the mapping function parameters for the mapping functions from the encoded video signal and applies the mapping functions as a part of display management operations to map the reference code values to the mapped pixel values appropriate for the media sink. The mapped pixel values can be used to render images as represented by the reference code values.
Abstract:
Techniques use multiple lower bit depth (e.g., 8 bits) codecs to provide higher bit depth (e.g., 12+ bits) high dynamic range images from an upstream device to a downstream device. Multiple layers comprising a base layer and one or more enhancement layers may be used to carry video signals comprising image data compressed by lower bit depth encoders to a downstream device, wherein the base layer cannot be decoded and viewed on its own. Lower bit depth input image data to base layer processing may be generated from higher bit depth high dynamic range input image data via advanced quantization to minimize the volume of image data to be carried by enhancement layer video signals. The image data in the enhancement layer video signals may comprise residual values, quantization parameters, and mapping parameters based in part on a prediction method corresponding to a specific method used in the advanced quantization. Adaptive dynamic range adaptation techniques take into consideration special transition effects, such as fade-in and fade-outs, for improved coding performance.
Abstract:
In an embodiment, a control map of false contour filtering is generated for a predicted image. The predicted image is predicted from a low dynamic range image mapped from the wide dynamic range image. Based at least in part on the control map of false contour filtering and the predicted image, one or more filter parameters for a sparse finite-impulse-response (FIR) filter are determined. The sparse FIR filter is applied to filter pixel values in a portion of the predicted image based at least in part on the control map of false contour filtering. The control map of false contour filtering is encoded into a part of a multi-layer video signal that includes the low dynamic range image.
Abstract:
Techniques use multiple lower bit depth codecs to provide higher bit depth, high dynamic range, images from an upstream device to a downstream device. A base layer and one or more enhancement layers may be used to carry video signals, wherein the base layer cannot be decoded and viewed on its own. Lower bit depth input image data to base layer processing may be generated from higher bit depth high dynamic range input image data via advanced quantization to minimize the volume of image data to be carried by enhancement layer video signals. The image data in the enhancement layer video signals may comprise residual values, quantization parameters, and mapping parameters based in part on a prediction method corresponding to a specific method used in the advanced quantization. Adaptive dynamic range adaptation techniques take into consideration special transition effects, such as fade-in and fade-outs, for improved coding performance.
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:
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:
An encoder receives one or more input pictures of enhanced dynamic range (EDR) to be encoded in a coded bit stream comprising a base layer and one or more enhancement layers. To encode the chroma pixels, the encoder generates a luma mask and a corresponding chroma mask. Based on generated high-clipping and low-clipping thresholds, the encoder determines the appropriate parameters to encode the chroma values in the base and enhancement layers.
Abstract:
A sequence of visual dynamic range (VDR) images is encoded using a standard dynamic range (SDR) base layer and one or more enhancement layers. A predicted VDR image is generated from an SDR input by using a weighted, multi-band, cross-color channel prediction model. Exponential weights with an adaptable decay parameter for each band are also presented.
Abstract:
An encoder receives a sequence of images in extended or visual dynamic range (VDR). For each image, a dynamic range compression function and associated parameters are selected to convert the input image into a second image with a lower dynamic range. Using the input image and the second image, a residual image is computed. The input VDR image sequence is coded using a layered codec that uses the second image as a base layer and a residual image that is derived from the input and second images as one or more residual layers. Using the residual image, a false contour detection method (FCD) estimates the number of potential perceptually visible false contours in the decoded VDR image and iteratively adjusts the dynamic range compression parameters to prevent or reduce the number of false contours. Examples that use a uniform dynamic range compression function are also described.