Abstract: A processor for approximating a reshaping function using a multi-segment polynomial receives an input reshaping function. Given a number of target segments (N) and an initial maximum fitting error, in a first pass, it applies a first smoothing filter to the input reshaping function to generate a first smoothed reshaping function. Next, it generates a first multi-segment polynomial approximation of the input reshaping function based on one or more multi-segment polynomial approximation algorithms, the smoothed reshaping function, the number of target segments, and the initial maximum fitting error. The same process may be repeated in two or more similar passes that may include in each pass: reconstructing the reshaping function from the polynomial approximation of the previous pass, smoothing and segmenting the reconstructed reshaping function, and generating an updated multi-segment polynomial approximation according to an updated maximum fitting error.

Abstract: In a method to reconstruct a high dynamic range video signal, a decoder receives parameters in the input bitstream to generate a prediction function. Using the prediction function, it generates a first set of nodes for a first prediction lookup table, wherein each node is characterized by an input node value and an output node value. Then, it modifies the output node values of one or more of the first set of nodes to generate a second set of nodes for a second prediction lookup table, and generates output prediction values using the second lookup table. Low-complexity methods to modify the output node value of a current node in the first set of nodes based on computing modified slopes between the current node and nodes surrounding the current node are presented.

Abstract: An SDR CDF is constructed based on an SDR histogram generated from a distribution of SDR codewords in SDR images. An HDR CDF is constructed based on an HDR histogram generated from a distribution of HDR codewords in HDR images that correspond to the SDR images. A histogram transfer function is generated based on the SDR CDF and the HDR CDF. The SDR images are transmitted along with backward reshaping metadata to recipient devices. The backward reshaping metadata is generated at least in part on the histogram transfer function.

Abstract: In a system for coding high dynamic range (HDR) images using lower-dynamic range (LDR) images, a reshaping function allows for a more efficient distribution of the codewords in the lower dynamic range images for improved compression. A trim pass of the LDR images by a colorist may satisfy a director's intent for a given “look,” but may also result in unpleasant clipping artifacts in the reconstructed HDR images. Given an original forward reshaping function which maps HDR luminance values to LDR pixel values, a processor identifies areas of potential clipping and generates modified forward and backward reshaping functions to reduce the visibility of potential artifacts from the trim pass process while preserving the director's intent.

Abstract: A processor for approximating a reshaping function using a multi-segment polynomial receives an input reshaping function. Given a number of target segments (N) and an initial maximum fitting error, in a first pass, it applies a first smoothing filter to the input reshaping function to generate a first smoothed reshaping function. Next, it generates a first multi-segment polynomial approximation of the input reshaping function based on one or more multi-segment polynomial approximation algorithms, the smoothed reshaping function, the number of target segments, and the initial maximum fitting error. The same process may be repeated in two or more similar passes that may include in each pass: reconstructing the reshaping function from the polynomial approximation of the previous pass, smoothing and segmenting the reconstructed reshaping function, and generating an updated multi-segment polynomial approximation according to an updated maximum fitting error.