Abstract: A content-adaptive quantizer processor receives an input image with an input bit depth. A noise-mask generation process is applied to the input image to generate a noise mask image which characterizes each pixel in the input image in terms of its perceptual relevance in masking quantization noise. A noise mask histogram is generated based on the input image and the noise mask image. A masking-noise level to bit-depth function is applied to the noise mask histogram to generate minimal bit depth values for each bin in the noise mask histogram. A codeword mapping function is generated based on the input bit depth, a target bit depth, and the minimal bit depth values. The codeword mapping function is applied to the input image to generate an output image in the target bit depth.

Abstract: A standard dynamic range (SDR) image is received. Composer metadata of the first level through the N-th level is generated. Composer metadata of the j-th level is generated based on the composer metadata of the first level through (j?1)-th level. The composer metadata of the first level through the composer metadata of the j-th level is to be used for mapping the SDR image to the j-th target image specifically optimized for the j-th reference target display. The SDR image is encoded with the composer metadata of the first level through the k-th level in an output SDR video signal, where 1<=k<=N. A display device renders a display image derived from a composed target image composed from the SDR image based on the composer metadata of the first level through the k-th level in the output SDR video signal.

Abstract: Given a standard-dynamic range (SDR) video input, techniques for generating and compressing composer metadata describing inverse luma and chroma reshaping functions are described. Given the SDR input, the composer metadata allow a decoder to generate a corresponding output in high-dynamic range. Three techniques are proposed: a static, sequence-based, architecture, a two-stage, scene-based, distributed solution with a centralized post-processing method, and a single-stage distributed solution using overlapped segments. Techniques to reduce the amount of transmitted composer metadata are also described.

Abstract: A first signal in a first color format is received. The first signal is transformed to a second signal in a second color format to be compressed using an encoder. Before encoding, the chroma components of the second signal are reshaped according to the statistical characteristics of the chroma components of the first signal to generate a third signal. In a decoder, an inverse reshaping functions is applied to the chroma components of the decoded signal to generate an approximation of the second signal.

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: Real-time forward reshaping, comprising selecting a statistical sliding window that indexes with the current frame, having also, a look-back frame and a look-ahead frame, determining whether they are part of the current scene, determining a noise parameter, a luma transfer function and a luma forward reshaping function based on the luma transfer function and the noise parameter within the current scene, selecting a central tendency sliding window of the current frame and the look-back frame within the current scene, and determining a central tendency luma forward reshaping function. The chroma reshaping comprises analyzing statistics for the extended dynamic range (EDR) weights and EDR upper bounds, mapping these to standard dynamic range (SDR) weights and SDR upper bounds based on the central tendency luma forward reshaping function, determining a chroma content-dependent polynomial and a central tendency chroma forward reshaping polynomial and generating chroma MMR coefficients.

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 surrounds the current node are presented.

Abstract: Methods and systems for chroma reshaping are applied to images or video frames. The method comprises receiving at least one image or video frame. The color space of the at least one image or video frame is partitioned in M1×M2×M3 non-overlapping bins. For each bin it is determined whether it is a valid bin, for which the at least one image or video frame has at least one pixel with a color value falling within said bin. For each chroma channel, a required number of codewords is calculated for representing two color values in said valid bin that have consecutive codewords for the respective chroma channel without a noticeable difference. At least one content-aware chroma forward reshaping function is generated based on the calculated required numbers of codewords and applied to the at least one image or video frame.

Abstract: An image processing device receives one or more forward reshaped images that are generated by an image forward reshaping device from one or more wide dynamic range images based on a forward reshaping function. The forward reshaping function relates to a backward reshaping function. The image processing device performs one or more image transform operations on the one or more forward reshaped images to generate one or more processed forward reshaped images without performing backward reshaping operations on the one or more reshaped images or the one or more processed forward reshaped images based on the backward reshaping function. The one or more processed forward reshaped images are sent to a second image processing device.

Abstract: In layered Visual Dynamic range (VDR) coding, inter-layer prediction requires several color-format transformations between the input VDR and Standard Dynamic Range (SDR) signals. Coding and decoding architectures are presented wherein inter-layer prediction is performed in the SDR-based color format, thus reducing computational complexity in both the encoder and the decoder, without compromising coding efficiency or coding quality.

Abstract: A method to improve the efficiency of coding high-dynamic range (HDR) signals in a dual-layer system is presented. A piece-wise linear, two-segment, inter-layer predictor is designed where base-layer codewords larger than a highlights threshold (Sh) are all mapped to a constant value. Given a target bit rate for the enhancement layer, which can be expressed as a percentage (?) of the bit rate of the base layer, an optimal highlights threshold is derived by computing estimated bit rates for the base and enhancement layers based on pixel complexity measures of pixels in the input HDR signal and the threshold value, and by minimizing an optimization criterion.

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: A tone-mapping function that maps input images of a high dynamic range into reference tone-mapped images of a relatively narrow dynamic range is generated. A luma forward reshaping function is derived, based on first bit depths and second bit depths, for forward reshaping luma codewords of the input images into forward reshaped luma codewords of forward reshaped images approximating the reference tone-mapped images. A chroma forward reshaping mapping is derived for predicting chroma codewords of the forward reshaped images. Backward reshaping metadata that is to be used by recipient devices to generate a luma backward reshaping function and a chroma backward reshaping mapping is transmitted with the forward reshaped images to the recipient devices. Techniques for the joint derivation of forward luma and chroma reshaping functions are also presented.

Abstract: Input minimal noise levels of are computed over input codeword bins based on image content in input images of an input bit depth. The minimal noise levels are adjusted to generate approximated minimal noise levels of a higher bit depth. The approximated minimal noise levels are used to generate per-bin bit depths over the input codeword bins. The input codeword bins are classified into first codeword bins that have relatively high risks of banding artifacts and some other input codeword bins that have relatively low or zero risks of banding artifacts based on the per-bin bit depths. Portions of bit depths from the other input codeword bins are moved to the first input codeword bins to generate modified per-bin bit depths. A forward reshaping function constructed from the modified per-bin bit depths is used to reshape the input images into reshaped images used to generate output images.

Abstract: Methods for screen-adaptive decoding of video with high dynamic range (HDR) are described. The methods combine the traditional compositing and display management steps into one screen-adaptive compositing step. Given decoded standard dynamic range (SDR) input data, metadata related to the prediction of output HDR data in a reference dynamic range, and the dynamic range of a target display, new output luma and chroma prediction functions are generated that map directly the input SDR data to output HDR data in the target dynamic range, thus eliminating the display management step.

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: 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: For each content-mapped frame of a scene, it is determined whether the content mapped frame is susceptible to object fragmentation with respect to texture in a homogeneous region based on statistical values derived from the content-mapped image and a source image mapped into the content-mapped image. The homogeneous region is a region of consistent texture in the source image. Based on a count of content-mapped frames susceptible to object fragmentation in homogeneous region, it is determined whether the scene is susceptible to object fragmentation in homogeneous region. If so, an upper limit for mapped codewords for a prediction function for predicting codewords of a predicted image from the mapped codewords in the content-mapped image is adjusted. Mapped codewords above the upper limit are clipped to the upper limit.

Abstract: Pixel data of a video sequence with enhanced dynamic range (EDR) are predicted based on pixel data of a corresponding video sequence with standard dynamic range (SDR) and an inter-layer predictor. Under a highlights clipping constrain, conventional SDR to EDR prediction is adjusted as follows: a) given a highlights threshold, the SDR to EDR predictor is adjusted to output a fixed output value for all input SDR pixel values larger than the highlights threshold, and b) given a dark-regions threshold, the residual values between the input EDR signal and its predicted value are set to zero for all input SDR pixel values lower than the dark-regions threshold. Example processes to determine the highlights and dark-regions thresholds and whether highlights clipping is occurring are provided.

Abstract: In a decoder, a processor extracts a control map of false contour filtering from a part of a multi-layer video signal that includes a low dynamic range image mapped from an original high-dynamic range (HDR) image. It determines one or more filter parameters for a sparse finite-impulse-response (FIR) filter, where the one or more filter parameters relate to at least in part on the control map of false contour filtering and a predicted image predicted from the low dynamic range image. It applies the sparse FIR filter to filter pixel values in a portion of the predicted image based at least in part on the control map of false contour filtering, and it reconstructs a version of the original HDR image based at least in part on the portion of the predicted image as filtered by the FIR filter.