Abstract: Sample data and metadata related to spatial regions in images may be received from a coded video signal. It is determined whether specific spatial regions in the images correspond to a specific region of luminance levels. In response to determining the specific spatial regions correspond to the specific region of luminance levels, signal processing and video compression operations are performed on sets of samples in the specific spatial regions. The signal processing and video compression operations are at least partially dependent on the specific region of luminance levels.

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: 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: An encoder receives an input enhanced dynamic range (EDR) image and a corresponding lower dynamic range (LDR) image to be coded at a given target rate. Before coding, a pre-dithering process is applied to the input LDR image to generate a dithered LDR image at a second bit depth, lower than its original bit depth. The pre-dithering process includes: generating uniformly-distributed noise, applying a spatial filter to the noise to generate low-pass or high-pass filtered noise, applying a temporal high pass or low pass filter to the spatially-filtered noise to generate output noise noise, adding the output noise to the input LDR image to generate a noise-enhanced LDR image, and quantizing the noise-enhanced image to generate the dithered LDR image. Selecting the characteristics of the dithering filters is based on both the target bit rate and luminance characteristics of the pixels in the input LDR image.

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 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: 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 crosspixel 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: Representation and coding of multi-view images using tapestry encoding are described for standard and enhanced dynamic ranges compatibility. A tapestry comprises information on a tapestry image, a left-shift displacement map and a right-shift displacement map. Perspective images of a scene can be generated from the tapestry and the displacement maps. Different methods for achieving compatibility are described.

Abstract: An input image is divided into non-overlapping regions. For each of the non-overlapping regions, first output data is predicted with a first prediction function, parameters related thereto and region-specific input image data. For each region with prior-predicted neighbor regions, a pixel border portion, adjacent to the neighbor region, is defined. For the pixels in the defined border portion, second output data is predicted with a second prediction function, parameters related thereto, input image data from the border portion of the current region, and input prediction parameter data from the neighbor region. The first output prediction data is fused with the second output data to predict a final set of output prediction values.

Abstract: A confidentiality preserving system and method for performing a rank-ordered search and retrieval of contents of a data collection. The system includes at least one computer system including a search and retrieval algorithm using term frequency and/or similar features for rank-ordering selective contents of the data collection, and enabling secure retrieval of the selective contents based on the rank-order. The search and retrieval algorithm includes a baseline algorithm, a partially server oriented algorithm, and/or a fully server oriented algorithm. The partially and/or fully server oriented algorithms use homomorphic and/or order preserving encryption for enabling search capability from a user other than an owner of the contents of the data collection. The confidentiality preserving method includes using term frequency for rank-ordering selective contents of the data collection, and retrieving the selective contents based on the rank-order.

Abstract: An encoder receives an input enhanced dynamic range (EDR) image to be coded in a layered representation. Input images may be gamma-coded or perceptually-coded using a bit-depth format not supported by one or more video encoders. The input image is remapped to one or more quantized layers to generate output code words suitable for compression using the available video encoders. Algorithms to determine optimum function parameters for linear and non-linear mapping functions are presented. Given a mapping function, the reverse mapping function may be transmitted to a decoder as a look-up table or it may be approximated using a piecewise polynomial approximation. A polynomial approximation technique for representing reverse-mapping functions and chromaticity translation schemes to reduce color shifts are also presented.

Abstract: A high dynamic range input video signal characterized by either a gamma-based or a perceptually-quantized (PQ) source electro-optical transfer function (EOTF) is to be compressed. Given a luminance range for an image-region in the input, for a gamma-coded input signal, a rate-control adaptation method in the encoder adjusts a region-based quantization parameter (QP) so that it increases in highlight regions and decreases in dark regions, otherwise, for a PQ-coded input, the region-based QP increases in the dark areas and decreases in the highlight areas.

Abstract: An encoder receives a target image in a standard dynamic range and a guide image in a high dynamic range, wherein both the target image and the guide image represent the same scene. A color transient improvement (CTI) filter is selected to predict a chroma component of a decoded version of the target image based on both the luma and chroma components of the target image and the guide image. The filtering coefficients for the CTI filter are computed by minimizing an error measurement (e.g., MSE) between pixel values of the decoded image and the guide image. The computed set of filtering coefficients is signaled to a receiver (e.g., as metadata). A decoder receives the coded image and the metadata, and applies the same CTI filter to the decoded image to generate an output image.

Abstract: A sparse FIR filter can be used to process an image in order to rectify imaging artifacts. In a first example application, the sparse FIR filter is applied as a selective sparse FIR filter that examines a set of selected neighboring pixels of an original pixel in order to identify smooth areas of the image and to selectively apply filtering to only the smooth areas of the image. The parameters of selective filtering are selected based on the characteristics of an inter-layer predictor. In a second example application, the sparse FIR filter is applied as an edge aware selective sparse FIR filter that examines additional neighboring pixels to the set of selected pixels in order to identify edges and carry out selective filtering of smooth areas of the image. Examples for detecting and removing banding artifacts during the coding of high-dynamic range images are provided.

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 scene change is determined using a first and a second video signal, each representing the same scene or content, but at a different color grade (such as dynamic range). A set of prediction coefficients is generated to generate prediction signals approximating the first signal based on the second signal and a prediction model. A set of prediction error signals is generated based on the prediction signals and the first signal. Then, a scene change is detected based on the characteristics of the prediction error signals. Alternatively, a set of entropy values of the difference signals between the first and second video signals are computed, and a scene change is detected based on the characteristics of the entropy values.

Abstract: A set of optimized operational parameter values is generated for performing decontouring operations on a predicted image. The predicted image is predicted from a first image mapped from a second image that has a higher dynamic range than the first image. Based on the set of optimized operational parameter values, smoothen operations and selection/masking based on a residual mask are performed on the predicted image. The set of optimized operational parameter values is encoded into a part of a multi-layer video signal that includes the first image, and can be used by a recipient decoder to generate a decontoured image based on the predicted image and reconstruct a version of the second image.

Abstract: An encoder receives an input enhanced dynamic range (EDR) image to be stored or transmitted using multiple coding formats in a layered representation. A layer decomposer generates a lower dynamic range (LDR) image from the EDR image. One or more base layer (BL) encoders encode the LDR image to generate a main coded BL stream and one or more secondary coded BL streams, where each secondary BL stream is coded in a different coding format than the main coded BL stream. A single enhancement layer (EL) coded stream and related metadata are generated using the main coded BL stream, the LDR image, and the input EDR image. An output coded stream includes the coded EL stream, the metadata, and either the main coded BL stream or one of the secondary coded BL streams. Computation-scalable decoding and display management processes for EDR images are also described.

Abstract: Input VDR images are received. A candidate set of function parameter values for a mapping function is selected from multiple candidate sets. A set of image blocks of non-zero standard deviations in VDR code words in at least one input VDR image is constructed. Mapped code values are generated by applying the mapping function with the candidate set of function parameter values to VDR code words in the set of image blocks in the at least one input VDR image. Based on the mapped code values, a subset of image blocks of standard deviations below a threshold value in mapped code words is determined as a subset of the set of image blocks. Based at least in part on the subset of image blocks, it is determined whether the candidate set of function parameter values is optimal for the mapping function to map the at least one input VDR image.

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.