Classifications

• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
  • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
  • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
  • H04N19/102—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or selection affected or controlled by the adaptive coding
  • H04N19/103—Selection of coding mode or of prediction mode
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
  • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
  • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
  • H04N19/102—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or selection affected or controlled by the adaptive coding
  • H04N19/103—Selection of coding mode or of prediction mode
  • H04N19/105—Selection of the reference unit for prediction within a chosen coding or prediction mode, e.g. adaptive choice of position and number of pixels used for prediction
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
  • G06F17/10—Complex mathematical operations
  • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
  • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
  • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
  • H04N19/134—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or criterion affecting or controlling the adaptive coding
  • H04N19/146—Data rate or code amount at the encoder output
  • H04N19/147—Data rate or code amount at the encoder output according to rate distortion criteria
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
  • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
  • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
  • H04N19/134—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or criterion affecting or controlling the adaptive coding
  • H04N19/157—Assigned coding mode, i.e. the coding mode being predefined or preselected to be further used for selection of another element or parameter
  • H04N19/16—Assigned coding mode, i.e. the coding mode being predefined or preselected to be further used for selection of another element or parameter for a given display mode, e.g. for interlaced or progressive display mode
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
  • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
  • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
  • H04N19/189—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the adaptation method, adaptation tool or adaptation type used for the adaptive coding
  • H04N19/192—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the adaptation method, adaptation tool or adaptation type used for the adaptive coding the adaptation method, adaptation tool or adaptation type being iterative or recursive
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
  • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
  • H04N19/30—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using hierarchical techniques, e.g. scalability
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
  • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
  • H04N19/90—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using coding techniques not provided for in groups H04N19/10-H04N19/85, e.g. fractals
  • H04N19/98—Adaptive-dynamic-range coding [ADRC]

Definitions

• the present invention relates generally to images. More particularly, an embodiment of the present invention relates to a multiple color channel, multiple regression predictor between high dynamic range images and standard dynamic range images.
• the term 'dynamic range' may relate to a capability of the human psychovisual system (HVS) to perceive a range of intensity (e.g., luminance, luma) in an image, e.g., from darkest darks to brightest brights.
• HVS human psychovisual system
• DR relates to a 'scene- referred' intensity.
• DR may also relate to the ability of a display device to adequately or approximately render an intensity range of a particular breadth. In this sense, DR relates to a 'display-referred' intensity.
• the term may be used in either sense, e.g. interchangeably.
• high dynamic range relates to a DR breadth that spans the some 14-15 orders of magnitude of the human visual system (HVS).
• HVS human visual system
• well adapted humans with essentially normal e.g., in one or more of a statistical, biometric or opthamological sense
• intensity range that spans about 15 orders of magnitude.
• Adapted humans may perceive dim light sources of as few as a mere handful of photons.
• VDR visual dynamic range
• 'variable dynamic range' may individually or interchangeably relate to the DR that is simultaneously perceivable by a HVS.
• VDR may relate to a DR that spans 5-6 orders of magnitude.
• VDR nonetheless represents a wide DR breadth.
• the term 'simultaneous dynamic range' may relate to VDR.
• LDR low dynamic range
• SDR standard dynamic range
• more modern display devices may be capable of rendering high definition (HD) content and/or content that may be scaled according to various display capabilities such as an image scaler.
• HD high definition
• some more modern displays are capable of rendering content with a DR that is higher than the SDR of conventional displays.
• some modern LCD displays have a backlight unit (BLU) that comprises a light emitting diode (LED) array.
• the LEDs of the BLU array may be modulated separately from modulation of the polarization states of the active LCD elements.
• This dual modulation approach is extensible (e.g., to N-modulation layers wherein N comprises an integer greater than two), such as with controllable intervening layers between the BLU array and the LCD screen elements.
• Their LED array based BLUs and dual (or N-) modulation effectively increases the display referred DR of LCD monitors that have such features.
• HDR displays as they are often called (although actually, their capabilities may more closely approximate the range of VDR) and the DR extension of which they are capable, in relation to conventional SDR displays represent a significant advance in the ability to display images, video content and other visual information.
• the color gamut that such an HDR display may render may also significantly exceed the color gamut of more conventional displays, even to the point of capably rendering a wide color gamut (WCG).
• WCG wide color gamut
• Scene related HDR or VDR and WCG image content such as may be generated by "next generation” movie and TV cameras, may now be more faithfully and effectively displayed with the "HDR" displays (hereinafter referred to as 'HDR displays').
• extending image DR typically involves a bifurcate approach.
• scene referred HDR content that is captured with a modern HDR capable camera may be used to generate an SDR version of the content, which may be displayed on conventional SDR displays.
• generating the SDR version from the captured VDR version may involve applying a global tone mapping operator (TMO) to intensity (e.g., luminance, luma) related pixel values in the HDR content.
• TMO global tone mapping operator
• intensity e.g., luminance, luma
• generating an SDR image may involve applying an invertible operator (or predictor) on the VDR data. To conserve bandwidth or for other considerations, transmission of the actual captured VDR content may not be a best approach.
• an inverse tone mapping operator inverted in relation to the original TMO, or an inverse operator in relation to the original predictor, may be applied to the SDR content version that was generated, which allows a version of the VDR content to be predicted.
• the predicted VDR content version may be compared to originally captured HDR content. For example, subtracting the predicted VDR version from the original VDR version may generate a residual image.
• An encoder may send the generated SDR content as a base layer (BL), and package the generated SDR content version, any residual image, and the iTMO or other predictors as an enhancement layer (EL) or as metadata.
• BL base layer
• EL enhancement layer
• Sending the EL and metadata, with its SDR content, residual and predictors, in a bitstream typically consumes less bandwidth than would be consumed in sending both the HDR and SDR contents directly into the bitstream.
• Compatible decoders that receive the bitstream sent by the encoder may decode and render the SDR on conventional displays.
• Compatible decoders however may also use the residual image, the iTMO predictors, or the metadata to compute a predicted version of the HDR content therefrom, for use on more capable displays. It is the purpose of this invention to provide novel methods for generating predictors that allow for the efficient coding, transmission, and decoding of VDR data using corresponding SDR data.
• FIG. 1 depicts an example data flow for a VDR-SDR system, according to an embodiment of the present invention
• FIG. 2 depicts an example VDR encoding system according to an embodiment of the present invention
• FIG. 3 depicts input and output interfaces of a multivariate multi-regression predictor, according to an embodiment of the present invention
• FIG. 4 depicts an example multivariate multi-regression prediction process according to an embodiment of the present invention
• FIG. 5 depicts an example process on deciding on the model of a multivariate multi-regression predictor according to an embodiment of the present invention
• FIG. 6 depicts an example image decoder with a predictor operating according to the embodiments of this invention.
• Inter-color image prediction based on multivariate multi-regression modeling is described herein. Given a pair of corresponding VDR and SDR images, that is, images that represent the same scene but at different levels of dynamic range, this section describes methods that allow an encoder to approximate the VDR image in terms of the SDR image and a multivariate multi-regression (MMR) predictor.
• MMR multivariate multi-regression
• Example embodiments described herein relate to coding images with high dynamic range.
• An embodiment creates an MMR predictor that allows a VDR image to be expressed in relation to its corresponding SDR representation.
• FIG. 1 depicts an example data flow in a VDR-SDR system 100, according to an embodiment of the present invention.
• An HDR image or video sequence is captured using HDR camera 110.
• the captured image or video is processed by a mastering process to create a target VDR image 125.
• the mastering process may incorporate a variety of processing steps, such as: editing, primary and secondary color correction, color transformation, and noise filtering.
• the VDR output 125 of this process represents the director' s intend on how the captured image will be displayed on a target VDR display.
• the mastering process may also output a corresponding SDR image 145, representing the director' s intend on how the captured image will be displayed on a legacy SDR display.
• the SDR output 145 may be provided directly from mastering circuit 120 or it may be generated with a separate VDR-to-SDR converter 140.
• the VDR 125 and SDR 145 signals are input into an encoder 130.
• Purpose of encoder 130 is to create a coded bitstream that reduces the bandwidth required to transmit the VDR and SDR signals, but also allows a corresponding decoder 150 to decode and render either the SDR or VDR signals.
• encoder 130 may be a layered encoder, such as one of those defined by the MPEG-2 and H.264 coding standards, which represents its output as a base layer, an optional enhancement layer, and metadata.
• metadata relates to any auxiliary information that is transmitted as part of the coded bitstream and assists a decoder to render a decoded image.
• metadata may include, but are not limited to, such data as: color space or gamut information, dynamic range information, tone mapping information, or MMR predictor operators, such as those described herein.
• a decoder 150 uses the received coded bitstreams and metadata to render either an SDR image or a VDR image, according to the capabilities of the target display.
• an SDR display may use only the base layer and the metadata to render an SDR image.
• a VDR display may use information from all input layers and the metadata to render a VDR signal.
• FIG. 2 shows in more detail an example implementation of encoder 130 incorporating the methods of this invention.
• SDR' denotes an enhanced SDR signal.
• SDR video today is 8-bit, 4:2:0, ITU Rec. 709 data.
• SDR' may have the same color space (primaries and white point) as SDR, but may use high precision, say 12-bits per pixel, with all color components at full spatial resolution (e.g.., 4:4:4 RGB). From FIG.
• SDR can easily be derived from an SDR' signal using a set of forward transforms that may include quantization from say 12 bits per pixel to 8 bits per pixel, color transformation, say from RGB to YUV, and color subsampling, say from 4:4:4 to 4:2:0.
• the SDR output of converter 210 is applied to compression system 220.
• compression system 220 can be either lossy, such as H.264 or MPEG-2, or lossless.
• the output of the compression system 220 may be transmitted as a base layer 225.
• predictor 250 may have the following inputs: VDR input 205 and either SDR' signal 245, which corresponds to the SDR' signal as it will be received by a corresponding decoder, or input SDR' 207.
• Predictor 250 using input VDR and SDR' data will create signal 257 which represents an approximation or estimate of input VDR 205.
• Adder 260 subtracts the predicted VDR 257 from the original VDR 205 to form output residual signal 265. Subsequently (not shown), residual 265 may also be coded by another lossy or lossless encoder, and may be transmitted to the decoder as an
• Predictor 250 may also provide the prediction parameters being used in the prediction process as metadata 255. Since prediction parameters may change during the encoding process, for example, on a frame by frame basis, or on a scene by scene basis, these metadata may be transmitted to the decoder as part of the data that also include the base layer and the enhancement layer..
• VDR 205 and SDR' 207 represent the same scene, but are targeting different displays with different characteristics, such as dynamic range and color gamut, it is expected that there is a very close correlation between these two signals.
• MMR multivariate, multi-regression
• FIG. 3 shows input and output interfaces of an MMR predictor 300 according to an example implementation of this invention.
• predictor 330 receives input vectors v 310 and s 320, representing VDR and SDR image data respectively, and outputs vector v 340, representing the predicted value of input v.
• color pixels may be in RGB, YUV, YCbCr, XYZ, or any other color representation. While equations (1-3) assume a three-color representation for each pixel in an image or video frame, as also shown later, the methods described herein can be easily extended to image and video representations with more than three color components per pixel, or to image representation where one of the inputs may have pixels with a different number of color representations than the other input.
• the first order prediction model can be expressed as:
• each color component is expressed as a linear combination of all color components in the input.
• this model takes into consideration all color components of a pixel and thus takes full advantage of any inter-color correlation and redundancy.
• V S'M (1) , (8)
• M (1) is a 4x3 matrix.
• M (1) may be referred to, interchangeably, as a multivariate operator or as a prediction matrix.
• this MMR system can formulate this MMR system as two different problems: (a) a least squares problem, or (b) a total least squares problem; both of which can be solved using well known numerical methods. For example, using a least squares approach, the problem of solving for M can be formulated as minimizing the residual or prediction mean squares error, or
• V is a px3 matrix formed using the corresponding VDR input data.
• S' T denotes the transpose of S'
• S' T S' is a 4x4 matrix
• M (1) can also be solved using a variety of alternative numerical techniques, including SVD, QR, or LU decompositions.
• MMR-2 Second Order Model
• Equation (4) represents a first order MMR prediction model.
• the second order prediction MMR model can be expressed as:
• M (2) is a 3x3 matrix
• Equation (12) can be simplified by using a single prediction matrix
• V S (2) M (2) , (17)
• Equation (14) can be solved using the same optimization and solutions that were described in the previous section.
• the first order prediction model of equation (4) can be enhanced to incorporate cross-multiplications between the color components of each pixel, as in :
• M (1) is a 3x3 matrix and n is a 1x3 vector, both as defined in equation (5), and.
• SC is a /?x(l+7) matrix and equation (25) can be solved using the same least squares solution described earlier.
• the first order MMR-IC model can be extended to include second order data well.
• second order data well For example,
• equation (27) are the same those defined before in equations (5-26).
• equation (27) can be simplified by using a simple prediction matrix
• V SC (2) ⁇ MC (2) , (32)
• K denotes the highest order of the MMR predictor.
• MMR-C-S Spatial Extension based on MMR
• the value of a predicted pixel v depends only on the corresponding, normally collocated, input values of S;.
• MMR- based prediction one may also benefit by taking into consideration data from neighbor pixels. This approach corresponds into integrating into the MMR model any linear-type of processing of the inputs in the spatial domain, such as FIR type filtering.
• v g (i ) ⁇ m g , x ,y, z (i )s g y (i )s b z (i ) + ⁇ ⁇ sm giXiy s g (i + x, j + y).
• MMR-1 first order MMR model
• M (1) is a 3x3 matrix and n is a 1x3 vector, both as defined in equation (5),
• Equation (39) can easily be extended to apply these methods to both other color components and alternative neighbor pixel configurations.
• equation (38) can easily be formulated as a system of linear equations
• V SD MD 1 , (40) which can be solved as described before.
• M (1) is now a 3x6 matrix and n is a 1x6 vector given by
• Equation (41) can be expressed using a single prediction matrix M (1) as s,.M (1) (47) where
• V is a px4 matrix
• M (1) is a 4x6 matrix.
• FIG. 4 shows an example process for multi-channel multiple regression prediction according to an example implementation of our invention.
• the process starts at step 410, wherein a predictor, such as predictor 250, receives the input VDR and SDR signals. Given the two inputs, in step 420, the predictor decides on which MMR model to select. As described before, the predictor can select among a variety of MMR models, including (but not necessarily limited to): first order (MMR-1), second order (MMR- 2), third order or higher, first order with cross-product (MMR-1 C), second order with cross product (MMR-2C), third order or higher with cross product, or any of the above models with added spatial extensions.
• MMR-1 first order
• MMR- 2 third order or higher
• MMR-1 C first order with cross-product
• MMR-2C second order with cross product
• the selection of the MMR model can be done using a variety of methods that take into considerations a number of criteria, including: prior knowledge on the SDR and VDR inputs, available computational and memory resources, and target coding efficiency.
• FIG. 5 shows an example implementation of step 420 based on the requirement of the residual to be lower than a pre-determined threshold.
• any MMR model can be represented a set of linear equations of the form
• V SM , (50) where M is a prediction matrix.
• step 430 M can be solved using a variety of numerical methods. For example, under the constrain to minimize the mean square of the residual between V and its estimate V ,
• step 440 using equation (50), the predictor outputs V and M.
• FIG. 5 shows an example process 420 for selecting an MMR model during the prediction.
• Predictor 250 may start in step 510 with an initial MMR model, such as the one that has been used in a prior frame or scene, for example the second-order model (MMR-2), or the simplest possible model, such as MMR- 1.
• MMR-2 second-order model
• MMR- 1 the second-order model
• the predictor computes the prediction error between input V and its predicted value.
• step 530 if the prediction error is lower than a given threshold, then the predictor selects the existing model and stops the selection process (540), otherwise, in step 550, investigates whether to use a more complex model.
• the predictor may decide to use MMR-2-C or MMR-2-C-S. As described before, this decision may depend on a variety of criteria, including the value of the prediction error, processing power requirements, and target coding efficiency. If it is feasible to use a more complex model, a new model is selected in step 560 and the process returns back to step 520. Otherwise, the predictor will use the existing model (540).
• Prediction process 400 can be repeated at a variety of intervals as deemed necessary to maintain coding efficiency while using available computing resources. For example, when coding video signals, process 400 can be repeated on a per predefined video slice size basis, for each frame, a group of frames, or whenever the prediction residual exceeds a particular threshold.
• Prediction process 400 can also use all available input pixels or a sub-sample of those pixels.
• one may use pixels only from every k-th pixel row and every k-th pixel column of the input data, where k is an integer equal or higher than two.
• one may decide to skip input pixels that are below a certain clipping threshold (for example, very close to zero) or pixels that are above a certain saturation threshold (for example, for n-bit data, pixel values that are very close to 2" - 1.)
• a certain clipping threshold for example, very close to zero
• a certain saturation threshold for example, for n-bit data, pixel values that are very close to 2" - 1.
• one may use a combination of such subsampling and thresholding techniques in order to reduce the pixel sample size and accommodate the computational constrains of a particular implementation.
• Embodiments of the present invention may be implemented either on an image encoder or an image decoder.
• FIG. 6 shows an example implementation of decoder 150 according to an embodiment of this invention.
• Decoding system 600 receives a coded bitstream that may combine a base layer 690, an optional enhancement layer (or residual) 665, and metadata 645, which are extracted following decompression 630 and miscellaneous inverse transforms 640.
• the base layer 690 may represent the SDR representation of the coded signal and the metadata 645 may include information about the MMR prediction model that was used in the encoder predictor 250 and the corresponding prediction parameters.
• metadata may include the identification of the model being used (for example, MMR-1 , MMR-2, MMR-2C, and the like) and all matrix coefficients associated with that specific model.
• predictor 650 Given base layer 690 s and the color MMR-related parameters extracted from the metadata 645, predictor 650 can compute predicted V 680 using any of the corresponding equation described herein. For example, if the identified model is MMR-
• V 680 can be computed using equation (32). If there is no residual, or the residual is negligible, the predicted value 680 can be output directly as the final VDR image.
• adder 660 the output of the predictor (680) is added to the residual 665 to output VDR signal 670.
• Embodiments of the present invention may be implemented with a computer system, systems configured in electronic circuitry and components, an integrated circuit (IC) device such as a microcontroller, a field programmable gate array (FPGA), or another configurable or programmable logic device (PLD), a discrete time or digital signal processor (DSP), an application specific IC (ASIC), and/or apparatus that includes one or more of such systems, devices or components.
• IC integrated circuit
• FPGA field programmable gate array
• PLD configurable or programmable logic device
• DSP discrete time or digital signal processor
• ASIC application specific IC
• the computer and/or IC may perform, control or execute instructions relating to MMR-based prediction, such as those described herein.
• the computer and/or IC may compute, any of a variety of parameters or values that relate to the MMR prediction as described herein.
• the image and video dynamic range extension embodiments may be implemented in hardware, software, firmware and various combinations thereof.
• Certain implementations of the invention comprise computer processors which execute software instructions which cause the processors to perform a method of the invention.
• processors in a display, an encoder, a set top box, a transcoder or the like may implement MMR-based prediction methods as described above by executing software instructions in a program memory accessible to the processors.
• the invention may also be provided in the form of a program product.
• the program product may comprise any medium which carries a set of computer-readable signals comprising instructions which, when executed by a data processor, cause the data processor to execute a method of the invention.
• Program products according to the invention may be in any of a wide variety of forms.
• the program product may comprise, for example, physical media such as magnetic data storage media including floppy diskettes, hard disk drives, optical data storage media including CD ROMs, DVDs, electronic data storage media including ROMs, flash RAM, or the like.
• the computer-readable signals on the program product may optionally be compressed or encrypted.
• a component e.g. a software module, processor, assembly, device, circuit, etc.
• reference to that component should be interpreted as including as equivalents of that component any component which performs the function of the described component (e.g., that is functionally equivalent), including components which are not structurally equivalent to the disclosed structure which performs the function in the illustrated example embodiments of the invention.

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