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.
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 layer. The encoder comprises a base layer quantizer (BLQ) and an enhancement layer quantizer (ELQ) and selects parameters of the BLQ and the ELQ by a joint BLQ-ELQ adaptation method which given a plurality of candidate sets of parameters for the BLQ, for each candidate set, computes a joint BLQ-ELQ distortion value based on a BLQ distortion function, an ELQ distortion function, and at least in part on the number of input pixels to be quantized by the ELQ. The encoder selects as the output BLQ parameter set the candidate set for which the computed joint BLQ-ELQ distortion value is the smallest. Example ELQ, BLQ, and joint BLQ-ELQ distortion functions are provided.
Abstract:
A visual dynamic range (VDR) coding system creates a sequence of VDR prediction images using corresponding standard dynamic range (SDR) images and a prediction function. For each prediction image, an encoder identifies one or more areas within the prediction image suitable for post-prediction filtering. For each identified post-prediction area, a post-prediction filtering mode is selected among one or more post-prediction filtering modes. The selected post-prediction filtering mode is applied to output a filtered prediction image. Information related to the post-prediction filtering areas and the selected corresponding post-prediction filtering modes may be communicated to a receiver (e.g., as metadata) for guided post-prediction filtering. Example post-prediction filtering modes that use low-pass averaging filtering or adaptive linear interpolation are also described.
Abstract:
A forward reshaping mapping is generated to map a source image to a corresponding forward reshaped image of a lower dynamic range. The source image is spatially downsampled to generate a resized image into which noise is injected to generate a noise injected image. The forward reshaping mapping is applied to map the noise injected image to generate a noise embedded image of the lower dynamic range. A video signal is encoded with the noise embedded image and delivered to a recipient device for the recipient device to render a display image generated from the noise embedded image.
Abstract:
Backward reshaping metadata prediction models are trained with training SDR images and corresponding training HDR images. Content creation user input to define user adjusted HDR appearances for the corresponding training HDR images is received. Content-creation-user-specific modified backward reshaping metadata prediction models are generated based on the trained prediction models and the content creation user input. The content-creation-user-specific modified prediction models are used to predict operational parameter values of content-creation-user-specific backward reshaping mappings for backward reshaping SDR images into mapped HDR images of at least one content-creation-user-adjusted HDR appearance.
Abstract:
A global index value is generated for selecting a global reshaping function for an input image of a relatively low dynamic range using luma codewords in the input image. Image filtering is applied to the input image to generate a filtered image. The filtered values of the filtered image provide a measure of local brightness levels in the input image. Local index values are generated for selecting specific local reshaping functions for the input image using the global index value and the filtered values of the filtered image. A reshaped image of a relatively high dynamic range is generated by reshaping the input image with the specific local reshaping functions selected using the local index values.
Abstract:
In a cloud-based system for encoding high dynamic range (HDR) video, each node receives a video segment and bumper frames. Each segment is subdivided into primary scenes and secondary scenes to derive scene-based forward reshaping functions that minimize the amount of reshaping-related metadata when coding the video segment, while maintaining temporal continuity among scenes processed by multiple nodes. Methods to generate scene-based forward and backward reshaping functions to optimize video coding and improve the coding efficiency of reshaping-related metadata are also examined.
Abstract:
Methods and systems for frame rate scalability are described. Support is provided for input and output video sequences with variable frame rate and variable shutter angle across scenes, or for input video sequences with fixed input frame rate and input shutter angle, but allowing a decoder to generate a video output at a different output frame rate and shutter angle than the corresponding input values. Techniques allowing a decoder to decode more computationally-efficiently a specific backward compatible target frame rate and shutter angle among those allowed are also presented.
Abstract:
Methods and systems for frame rate scalability are described. Support is provided for input and output video sequences with variable frame rate and variable shutter angle across scenes, or for input video sequences with fixed input frame rate and input shutter angle, but allowing a decoder to generate a video output at a different output frame rate and shutter angle than the corresponding input values. Techniques allowing a decoder to decode more computationally-efficiently a specific backward compatible target frame rate and shutter angle among those allowed are also presented.
Abstract:
Methods and systems for frame rate scalability are described. Support is provided for input and output video sequences with variable frame rate and variable shutter angle across scenes, or for input video sequences with fixed input frame rate and input shutter angle, but allowing a decoder to generate a video output at a different output frame rate and shutter angle than the corresponding input values. Techniques allowing a decoder to decode more computationally-efficiently a specific backward compatible target frame rate and shutter angle among those allowed are also presented.