Apparatus and method for improving perceptual luminance nonlinearity-based image data exchange between different display functions

Abstract

To provide a device and method for improving perceptual nonlinearity-based image data exchange across different display capabilities.SOLUTION: A handheld image processor has a data receiver that is configured to receive reference encoded image data. The data includes reference code values, which are encoded by an external coding system. The reference code values represent reference gray levels, which are selected using a reference grayscale display function based on perceptual non-linearity of human vision adapted at different light levels to spatial frequencies. The image processor also has a data converter that is configured to access a code mapping between the reference code values and device-specific code values of the image processor. The device-specific code values are configured to produce gray levels that are specific to the image processor. Based on the code mapping, the data converter is configured to transcode the reference encoded image data into device-specific image data, which is encoded with the device-specific code values.SELECTED DRAWING: Figure 5

Description

[Technical Field]

[0001] Cross-references to related applications This application claims priority to U.S. Provisional Patent Application No. 61 / 567,579, filed on 6 December 2011, U.S. Provisional Patent Application No. 61 / 674,503, filed on 23 July 2012, and U.S. Provisional Patent Application No. 61 / 703,449, filed on 12 September 2012. The contents of these applications are incorporated herein by reference in whole for all purposes.

[0002] The technology of the present invention This invention broadly relates to image data. More specifically, one embodiment of the invention relates to the exchange of image data based on perceptual nonlinearity between different display functions. [Background technology]

[0003] Technological advancements have enabled modern display designs to render image and video content with significant improvements in various quality characteristics compared to the same content rendered on displays from a few years ago. For example, some newer displays can render content with a higher dynamic range (DR) than the standard dynamic range (SDR) of typical or standard displays.

[0004] For example, some modern liquid crystal displays (LCDs) have illumination units (backlight units, sidelight units, etc.) that provide a light field that can modulate individual parts independently of the modulation of the liquid crystal alignment state of the active LCD elements. This dual modulation technique can be extended by controllable intervening layers in the electro-optical configuration of the display (e.g., multiple individually controllable LCD layers) (e.g., N modulation layers, where N is an integer greater than 2).

[0005] In contrast, some existing displays have a significantly narrower dynamic range (DR) than high dynamic range (HDR). Typical cathode ray tubes (CRTs), liquid crystal displays (LCDs) with a constant fluorescent white backlight, or mobile devices, computer pads, game consoles, televisions (TVs), and computer monitors using plasma screen technology can be limited to nearly three orders of magnitude in their DR rendering capabilities. Thus, such existing displays are typical of standard dynamic range (SDR), sometimes referred to as "low dynamic range (LDR)" in relation to HDR. [Prior art documents] [Non-patent literature]

[0006] [Non-Patent Document 1] Peter GJ Barten, Contrast Sensitivity of the Human Eye and its Effects on Image Quality (1999) [Non-Patent Document 2] Scott Daly, Digital Images and Human Vision, AB Watson (ed.), MIT Press (1993) [Overview of the project] [Problems that the invention aims to solve]

[0007] Images captured by HDR cameras may have scene-referred HDR, which has a significantly larger dynamic range than most, if not all, display devices. Scene-referred HDR images contain a large amount of data and may be converted to post-production formats (e.g., HDMI® video signals with 8-bit RGB, YCbCr, or deep color options; 1.5Gbps SDI video signals with a 10-bit 4:2:2 sampling rate; 3Gbps SDI with a 12-bit 4:4:4 or 10-bit 4:2:2 sampling rate; and other video or image formats) to facilitate transmission and storage. Post-production images have a much smaller dynamic range than scene-referred HDR images. Furthermore, as the image is delivered to the end-user's display device for rendering, device-specific and / or manufacturer-specific image conversions occur along the way, causing a large amount of visually perceptible error in the rendered image compared to the original scene-referred HDR image. [Means for solving the problem]

[0008] The approaches described in the above sections could have been pursued, but are not necessarily previously conceived or pursued. Therefore, unless otherwise noted, none of the approaches described in those sections should be assumed to qualify as prior art simply because they are included in those sections. Similarly, unless otherwise noted, no problem identified with respect to one or more approaches should be assumed to have been recognized in any prior art based on those sections. [Brief explanation of the drawing]

[0009] The present invention is illustrated, not limited, in examples, as shown in the accompanying drawings. In the drawings, similar reference numerals refer to similar elements. [Figure 1]This figure shows an exemplary family of contrast sensitivity function curves over multiple light-adaptive levels, based on an exemplary embodiment of the present invention. [Figure 2] This figure shows an exemplary integration path based on an exemplary embodiment of the present invention. [Figure 3] This figure shows an exemplary grayscale display function based on an exemplary embodiment of the present invention. [Figure 4] This is a curve depicting the Weber ratio, based on an exemplary embodiment of the present invention. [Figure 5] This figure shows an exemplary framework for exchanging image data using various GSDF devices, based on an exemplary embodiment of the present invention. [Figure 6] This figure shows an exemplary conversion unit based on an exemplary embodiment of the present invention. [Figure 7] This figure shows an exemplary SDR display based on an exemplary embodiment of the present invention. [Figure 8] Figures A and B show an exemplary process flow based on an exemplary embodiment of the present invention. [Figure 9] This figure shows an exemplary hardware platform on which a computer or computing device described in this paper may be implemented, based on an exemplary embodiment of the present invention. [Figure 10A] This figure shows the maximum code error in JND units in multiple code spaces, each having one or more different bit lengths, based on several exemplary embodiments. [Figure 10B] This figure shows the distribution of sign errors based on several exemplary embodiments. [Figure 10C] This figure shows the distribution of sign errors based on several exemplary embodiments. [Figure 10D] This figure shows the distribution of sign errors based on several exemplary embodiments. [Figure 10E] This figure shows the distribution of sign errors based on several exemplary embodiments. [Figure 11] This figure shows the parameter values ​​in a function model based on an exemplary embodiment. [Modes for carrying out the invention]

[0010] This paper describes exemplary embodiments relating to the exchange of image data based on perceptual luminance nonlinearity between displays of different functions. Numerous specific details are described below for illustrative purposes to provide a full understanding of the invention. However, it will be apparent that the invention can be carried out without such specific details. On the other hand, well-known structures and apparatus are not described in exhaustive detail to avoid unnecessarily obscuring, burying, or ambiguizing the invention.

[0011] Exemplary embodiments are described in this paper according to the following outline.

[0012] 1. Overview 2. Contrast Sensitivity Function (CSF) Model 3. Nonlinearity of Perception 4. Digital code values ​​and gray levels 5. Model Parameters 6. Variable spatial frequency 7. Functional Models 8. Exchange of image data based on the Reference GSDF 9. Converting the reference-encoded image data 10. Exemplary process flow 11. Implementation Mechanisms - Hardware Overview 12. Numbering examples, equivalents, extensions, substitutes, and others. <1. Overview> This overview provides a basic description of several aspects of a particular embodiment of the present invention. It should be noted that this overview is not a comprehensive or exhaustive summary of the aspects of the exemplary embodiment. Furthermore, this overview is not intended to be understood as identifying any particularly significant aspects or elements of the exemplary embodiment, nor as defining any scope of the invention, particularly of the exemplary embodiment. This overview simply presents some concepts relating to the exemplary embodiment in a condensed and simplified form, and should be understood merely as a conceptual introduction to a more detailed description of the subsequent exemplary embodiments.

[0013] Human vision may not perceive a difference between two luminance values ​​if they are not sufficiently different. Human vision only perceives a difference when luminance values ​​differ by at least a just noticeable difference (JND). Due to the nonlinearity of human vision perception, the magnitude of an individual JND is not uniform across a range of light levels, nor is it uniformly scaled; rather, it varies with different individual light levels. Furthermore, due to the nonlinearity of perception, the magnitude of an individual JND is not uniform across a range of spatial frequencies at a particular light level, nor is it uniformly scaled; rather, it varies with different spatial frequencies below a certain cutoff spatial frequency.

[0014] Encoded image data with luminance quantization steps of equal size or linearly scaled size does not correspond to the perceptual non-uniformity of human vision. Encoded image data with luminance quantization steps at fixed spatial frequencies does not correspond to the perceptual non-uniformity of human vision. Under these techniques, when codewords are assigned to represent quantized luminance values, too many codewords may be distributed in a particular region (e.g., a bright region) within the above range of light levels, while too few codewords may be distributed in a different region (e.g., a dark region) within the above range of light levels.

[0015] In over-distribution regions, many codewords may not produce perceptual differences and are therefore essentially wasted. In under-distribution regions, two adjacent codewords may produce a much greater perceptual difference than JNDs, potentially leading to contour distortion (also known as banding) and other visual artifacts.

[0016] Under the techniques described in this paper, a wide range of light levels (e.g., 0 to 12,000 cd / m²) can be used. 2A contrast sensitivity function (CSF) model may be used to determine the JND over a range of light levels. In one exemplary embodiment, the peak JND as a function of spatial frequency at a particular light level is selected to represent the quantum of human perception at that particular light level. The selection of the peak JND matches the behavior of human vision to adapt to an enhanced level of visual perceptibility when viewing a background of similar but different luminance values. This is sometimes referred to as the crispening effect and / or Whittle's crispening effect in the field of video and image display, and will be described as such in this paper. In the usage of this paper, "light adaptation level" may be used to refer to the light level at which the (e.g., peak) JND is selected / determined, assuming that human vision is adapted to that light level. The peak JNDs described in this paper vary over spatial frequency at various light adaptation levels.

[0017] In the context of this paper, the term “spatial frequency” may refer to the rate of spatial modulation / variation in an image (where the rate is calculated in relation to or against spatial distance, rather than against time). In contrast to the usual approach where spatial frequency can be fixed to a specific value, the spatial frequency described in this paper can vary, for example, within or over a certain range. In some embodiments, the peak JND may be limited to a specific spatial frequency range (e.g., 0.1 to 5.0, 0.01 to 8.0 cycles / degree, or a smaller or larger range).

[0018] Based on the CSF model, a reference grayscale display function (GSDF) can be generated. In some embodiments, a very broad view of the CSF model is assumed to generate a reference GSDF that better supports the entertainment display field. A GSDF refers to a set of reference digital code values ​​(or reference codewords), a set of reference gray levels (or reference luminance values), and a mapping between the two sets. In one exemplary embodiment, each reference digital code value corresponds to a quantum of human perception represented by a JND (e.g., peak JND at the light adaptive level). In one exemplary embodiment, an equal number of reference digital code values ​​may correspond to quanta of human perception.

[0019] The GSDF is obtained by accumulating JNDs from an initial value. In one exemplary embodiment, the central codeword value (e.g., 2048 for a 12-bit code space) is given as the initial value for the reference digital code. The initial value for the reference digital code is the initial reference gray level (e.g., 100 cd / m²). 2 ) may correspond to other reference gray levels for other values ​​of the reference digital code, obtained by positively accumulating (adding) the JND when the reference digital code is incremented one by one, and by negatively accumulating (subtracting) the JND when the reference digital code is decremented one by one. In one exemplary embodiment, a quantity such as a contrast threshold may be used instead of the JND in the calculation of the reference value in the GSDF. These quantities actually used in the calculation of the GSDF may be defined as unitless ratios and may differ from the corresponding JND only by a known or determinable multiplier, divisor and / or offset.

[0020] The code space may be selected to contain all reference digital code values ​​in the GSDF. In some embodiments, the code space in which all reference digital code values ​​reside may be a 10-bit code space, an 11-bit code space, a 12-bit code space, a 13-bit code space, a 14-bit code space, a 15-bit code space, or one of the larger or smaller code spaces.

[0021] A large code space (>15 bits) may be used to accommodate all reference digital code values, but in certain embodiments, the most efficient code space (e.g., 10 bits, 12 bits, etc.) is used to accommodate all reference digital code values ​​generated in the reference GSDF.

[0022] Reference GSDF may be used to encode image data captured or generated by, for example, an HDR camera, a studio / system, or other system with scene-referenced HDR that is significantly larger than the dynamic range of most, if not all, display devices. The encoded image data may be delivered to downstream devices via a wide variety of delivery or transmission methods (e.g., HDMI® video signals with 8-bit RGB, YCbCr, or deep color options; 1.5 Gbps SDI video signals with a 10-bit 4:2:2 sampling rate; 3 Gbps SDI with a 12-bit 4:4:4 or 10-bit 4:2:2 sampling rate; and other video or image formats).

[0023] In some embodiments, adjacent reference digital code values ​​in the reference GSDF correspond to gray levels within JND, so that in image data encoded based on the reference GSDF, human visually distinguishable details can be fully or substantially preserved. A display that fully supports the reference GSDF may be able to render images without banding or contour distortion artifacts.

[0024] Image data encoded based on a reference GSDF (or reference encoded image data) may be used to support a wide variety of less powerful displays that may not fully support all reference luminance values ​​in the reference GSDF. Since the reference encoded image data contains all perceptual detail within the supported luminance range (which may be designed to be a superset of those supported by the display), the reference digital code values ​​may be transcoded to display-specific digital code values ​​in an optimal and efficient manner in such a way that preserves as much detail as a particular display can support and causes as few visually perceptible errors as possible. Additionally and / or optionally, decontouring and dithering may be performed in connection with, or as part of, the transcoding from reference digital code values ​​to display-specific digital code values ​​to further improve image or video quality.

[0025] The techniques described in this paper are color space independent. They can be used in the RGB color space, the YCbCr color space, or any other color space. Furthermore, the techniques for deriving reference values ​​(e.g., reference digital code value and reference gray level) using JNDs that vary with spatial frequency may be applied to a different channel other than the luminance channel (e.g., one of the red, green, and blue channels) in a different color space (e.g., RGB) that may or may not include a luminance channel. For example, a reference blue value may be derived instead of a reference gray level using a JND applicable to the blue color channel. Thus, in some embodiments, grayscale may be used instead of color. Additionally and / or optionally, a different CSF model may be used instead of Barten's model, and different model parameters may be used for the same CSF model.

[0026] In some embodiments, the mechanisms described herein form part of a media processing system that includes, but is not limited to, handheld devices, game consoles, televisions, laptop computers, netbook computers, cellular wireless telephones, e-book readers, point-of-sale terminals, desktop computers, computer workstations, computer kiosks, or various other types of terminals and media processing units.

[0027] Various modifications to the preferred embodiments and general principles and features described herein will be obvious to those skilled in the art. Therefore, this disclosure is not intended to be limited to the embodiments shown, but should be given the broadest scope consistent with the principles and features described herein.

[0028] <2. Contrast Sensitivity Function (CSF) Model> The human visual sensitivity to spatial structure in rendered images can be best described by a contrast sensitivity function (CSF). This describes contrast sensitivity as a function of spatial frequency (or the rate of spatial modulation / variation in the image as perceived by a human observer). In the context of this paper, contrast sensitivity S can be thought of as the gain in the neural signal processing of human vision, while contrast threshold C T This may be determined from the reciprocal of the contrast sensitivity. For example: Contrast sensitivity = S = 1 / C T (1) In the context of this paper, the "contrast threshold" refers to or may relate to the minimum (relative) contrast (e.g., minimum discernible difference) required for the human eye to perceive a difference in contrast. In some embodiments, the contrast threshold may be described as a function of the minimum discernible difference divided by the light-adaptive level over a range of luminance values.

[0029] In some embodiments, the contrast threshold may be directly measured in an experiment without using any CSF model. However, in some other embodiments, the contrast threshold may be determined based on a CSF model. The CSF model may be constructed using several model parameters and may be used to derive the GSDF. The quantization step of the GSDF in terms of gray levels depends on and varies with the light level and spatial frequency characterized by the luminance value. An exemplary embodiment may be implemented based on one or more of various CSF models such as those described in Non-Patent Document 1 (hereinafter, Barten's model or Barten's CSF model) or those described in Non-Patent Document 2 (hereinafter, Daily's model). In the context of an exemplary embodiment of the present invention, the contrast threshold used to generate the reference gray scale display function (GSDF) may be derived experimentally, theoretically, using a CSF model, or a combination thereof.

[0030] In the usage in this paper, the GSDF may refer to the mapping of a plurality of digital code values (e.g., 1, 2, 3,..., N) to a plurality of gray levels (L1, L2, L3,..., L N ). Here, as shown in Table 1, the digital code value represents the index value of the contrast threshold, and the gray level corresponds to the contrast threshold.

[0031]

Table 1

[0032] C(i)=(L i+1 -L i ) / (L i+1 +L i ) =(L i+1 -Lmean (i,i+1)) / L mean (i, i+1) = (1 / 2)ΔL / L (2) Here, C(i) is L i and L i+1 This represents the contrast over the luminance range with L as both ends. mean (i,i+1) represents two adjacent gray levels L i and L i+1 This is the arithmetic mean of . The contrast C(i) is arithmetically related to the Weber fraction by factor 2. Here, ΔL is (L i+1 -L i ) represents, and L is L i , L i+1 One of or L i and L i+1 It represents an intermediate value.

[0033] In some embodiments, the GSDF generator calculates the contrast C(i) including both ends of L as follows: i and L i+1 The contrast threshold at a certain luminance level L (e.g., C) T (i)) may be set to a value equal to or proportional to it.

[0034] C(i) = kC T (i) (3) Here, k represents a multiplication constant. In relation to embodiments of the present invention, other descriptive statistics / definitions (e.g., geometric mean, medium, mode, variance, or standard deviation) and / or scaling (e.g., ×2, ×3, divide by a scaling factor, or multiply by a scaling factor) and / or offset (e.g., +1, +2, -1, -2, subtract or add an offset) and / or weighting (e.g., assigning the same or different weighting factors to two adjacent gray levels) may be used to relate the contrast threshold to contrast for the purpose of calculating gray levels in the GSDF.

[0035] As calculated by equations (1), (2), and (3), contrast or contrast threshold is a relative value and therefore can be a dimensionless quantity. (Thus, for example, S can also be a dimensionless quantity.) CSF models can be constructed from basic contrast threshold measurements or calculations based on CSF that plot the CSF model. Unfortunately, human vision is complex, adaptive, and nonlinear, and therefore there is no single CSF curve that describes human vision. Instead, a family of CSF curves can be generated based on CSF models. Even with the same CSF model, different values ​​of the model parameters will produce different plots for that family of CSF curves.

[0036] <3. Nonlinearity of Perception> Figure 1 shows an exemplary family of CSF curves spanning multiple light adaptation levels. For illustrative purposes only, the uppermost CSF curve shown in Figure 1 represents 1000 candelas per square meter (cd / m²). 2 The first curve (or "nit") represents the level of light adaptation at a given luminance value, while the other lower curves represent the level of light adaptation at luminance values ​​decreasing tenfold. A notable feature that can be observed from these CSF curves is that as luminance increases (as the level of light adaptation increases), the overall contrast sensitivity, including the maximum (or peak) contrast sensitivity, increases. On the CSF curves in Figure 1, the peak spatial frequency at which contrast sensitivity peaks shifts to a higher spatial frequency. Similarly, the maximum perceptible spatial frequency (cutoff frequency) on the CSF curve, which is the intercept of the CSF curve with the horizontal axis (spatial frequency axis), also increases.

[0037] In one exemplary embodiment, the CSF function giving the family of CSF curves shown in Figure 1 may be derived using Bartenberg's CSF model, which takes into account several key effects related to human perception. Exemplary CSF S(u) (or corresponding contrast threshold m) under Bartenberg's CSF model t The reciprocal of may be calculated as shown in equation (4) below.

[0038]

number

[0039] Optical modulation transfer function (M) opt It may also be given as follows:

[0040]

number

[0041] The Bartender CSF model discussed above can be used to describe the nonlinearity of perception with respect to luminance. Other CSF models may be used to describe the nonlinearity of perception. For example, the Bartender CSF model does not take into account the effect of accommodation, which causes a decrease in the cutoff spatial frequency of the CSF in the high spatial frequency domain. This decrease due to accommodation may be expressed as a function of the decreasing viewing distance.

[0042] For example, for viewing distances exceeding 1.5 meters, the maximum cutoff spatial frequency described by Bartner's CSF model can be reached, but this does not affect the effectiveness of Bartner's model as a suitable model for describing the nonlinearity of perception. However, for distances shorter than 1.5 meters, the effect of accommodation begins to become significant, reducing the accuracy of Bartner's model.

[0043] Thus, for tablet displays with closer viewing distances, such as 0.5 meters, and smartphones with viewing distances as close as 0.125 meters, the Bartender CSF model may not be optimally tuned.

[0044] In some embodiments, Daly's CSF model may be used, which takes into account the near-far accommodation effect. In certain embodiments, Daly's CSF model is based in part on Bartender's CSF S(u) of equation (4) above, for example, on the optical modulation transfer function M of equation (5). opt It may be configured by modifying it.

[0045] <4. Digital Code Values ​​and Gray Levels> The GSDF, as shown in Table 1, uses digital code values ​​to map perceptual nonlinearity to represent gray levels linked to contrast thresholds in human vision. The gray levels, including all mapped luminance values, may be distributed in a manner that is optimally spaced to match the perceptual nonlinearity of human vision.

[0046] In some embodiments, when the maximum number of gray levels in the GSDF is sufficiently large relative to the maximum range of luminance values, the digital code values ​​in the GSDF may be used in such a manner that the minimum number of gray levels (e.g., fewer than 4096 digital code values ​​in total) is achieved without causing the visibility of gray level step transitions (e.g., appearing as false contours or bands in the image; or color shifts in dark areas of the image).

[0047] In some other embodiments, a limited number of digital code values ​​may be used to represent gray levels with a wide dynamic range. For example, when the maximum number of grayscale levels in a GSDF is not sufficiently large for the maximum range of grayscale levels (e.g., 8-bit digital code values ​​for a grayscale level range of 0 to 12,000 nits), the GSDF may still be used in such a way as to achieve a minimum number of gray levels (e.g., fewer than 256 digital code values ​​in total) to reduce or minimize the visibility of gray-level step transitions. In such a GSDF, the amount / degree of perceptible error / artifact of step transitions may be evenly distributed through a relatively small hierarchy of gray levels in the GSDF. In the usage of this paper, the terms “grayscale level” or “gray level” may be used interchangeably and may refer to the expressed luminance value (the quantized luminance value expressed in the GSDF).

[0048] The gray levels in the GSDF can be derived by accumulating or integrating the contrast threshold across various optical adaptive levels (at different luminance values). In some embodiments, the quantization steps between gray levels may be chosen such that the quantization steps between any two adjacent gray levels fall within the JND. The contrast threshold at a particular optical adaptive level (or luminance value) may not be greater than the minimum known difference (JND) at that particular adaptive level. The gray levels may be derived by accumulating or accumulating the fraction of the contrast threshold (or JND). In some embodiments, the number of digital sign values ​​is greater than is sufficient to represent all JNDs in the dynamic range of luminance being expressed.

[0049] The contrast threshold, or conversely, the contrast sensitivity, used to calculate the grayscale level may be selected from CSF curves at different spatial frequencies other than a fixed spatial frequency for a particular light adaptation level (or luminance value). In some embodiments, each contrast threshold is selected from a CSF curve at a spatial frequency corresponding to the peak contrast sensitivity (for example, due to Whittle's sharpening effect) for a light adaptation level. Furthermore, the contrast threshold may be selected from CSF curves at various spatial frequencies for various light adaptation levels.

[0050] An example of a formula for calculating / accumulating gray levels in GSDF is as follows:

[0051]

number

[0052] In some embodiments, the integration of JNDs to derive the gray level in the GSDF is performed by summation, as shown, for example, in equation (6). In some other embodiments, integration may be used instead of discrete summation. The integration may be performed along an integration path determined from the CSF (for example, equation (4)). For example, the integration path may include peak contrast sensitivities for all optical adaptive levels in the (reference) dynamic range for the CSF (e.g., different peak sensitivities corresponding to different spatial frequencies).

[0053] In the context of this paper, the integral path may refer to a visible dynamic range (VDR) curve used to represent the nonlinearity of human perception and to establish a mapping between a set of digital sign values ​​and a set of reference gray levels (quantized luminance values). The mapping may require that each quantization step (e.g., the luminance difference between two adjacent gray levels in Table 1) is less than or equal to the JND above or below the corresponding light adaptation level (luminance value). The instantaneous derivative of the integral path at a particular light adaptation level (luminance value) (in units of nits / spatial cycles) is proportional to the JND at that particular adaptation level. In the context of this paper, "VDR" or "visual dynamic range" may refer to a dynamic range wider than the standard dynamic range, and may include, but is not limited to, the instantaneously perceptible dynamic range and color range that human vision can perceive at any given moment.

[0054] Based on the techniques described in this paper, a reference GSDF independent of any particular display or image processing device may be developed. In some embodiments, one or more model parameters other than the light adaptive level (luminance), spatial frequency, and angular size may be set to a constant (or fixed) value.

[0055] <5. Model Parameters> In some embodiments, the CSF model is constructed with conservative model parameter values ​​that cover a wide range of display devices. The use of conservative model parameter values ​​provides a smaller JND than existing standard GSDFs. Thus, in some embodiments, the reference GSDF under the techniques described herein can support luminance values ​​with higher precision than the requirements of these display devices.

[0056] In some embodiments, the model parameters described herein include a field-of-vision (FOV) parameter. The FOV parameter may be set to values ​​of 45 degrees, 40 degrees, 35 degrees, 30 degrees, 25 degrees, or other larger or smaller values ​​to support a wide range of display and viewing scenarios, including those used in studios, theaters, or high-end entertainment systems.

[0057] The model parameters described in this paper may include an angular size parameter, which may relate, for example, to the field of view. The angular size parameter may be set to values ​​of 45°×45°, 40°×40°, 35°×35°, 30°×30°, 25°×25°, or other larger or smaller values ​​to support studio, wide range of display devices, and viewing scenarios. In some embodiments, the angular size parameter used in part to derive the reference GSDF is set to n°×m°, where n and m may both be numerical values ​​between 30 and 40, and n and m may be equal or unequal.

[0058] In some embodiments, a larger angular size (e.g., 40° x 40°) is used to produce a reference GSDF with more grayscale levels and therefore greater contrast sensitivity. This GSDF may be used to support a wide range of viewing and / or display scenarios (e.g., large-screen video displays) that may require a wide viewing angle of ~30 to 40 degrees. The GSDF, with its increased sensitivity due to the larger angular size, may be used to support a wide range of viewing and display scenarios (e.g., movie theaters). While even larger angular sizes are possible, the benefits of significantly increasing the angular size above a certain angular size (e.g., 40°) may be relatively limited.

[0059] In some embodiments, the reference GSDF model covers a wide luminance range. For example, the gray level or quantized luminance value represented by the reference GSDF model is 0 or approximately 0 (e.g., 10). -7 cd / m 2 ) to 12,000 cd / m² 2 It spans the range. The lower limit of the luminance values ​​expressed in the reference GSDF model is 10 -7 cd / m 2 or a lower or higher value (for example, 0, 10) -5 , 10 -8 , 10 -9 cd / m 2 (and so on). The GSDF can be used to support a wide range of viewing and / or display scenarios with varying ambient light levels. The GSDF can be used to support a wide range of display devices with varying dark black levels (in theaters, indoors, or outdoors).

[0060] The upper limit of the luminance value expressed in the reference GSDF model is 12,000 cd / m². 2 Or lower or higher values ​​(for example, 6000-8000, 8000-10000, 10000-12000, 12000-15000 cd / m²) 2 GSDF may also be used to support a wide range of viewing and / or display scenarios with high dynamic range. GSDF may also be used to support a wide range of display devices with varying maximum luminance levels (e.g., HDR TVs, SDR displays, laptops, tablets, handheld devices).

[0061] <6. Variable spatial frequency> Figure 2 shows an exemplary integration path (denoted as VDR) that may be used as an integration path to obtain the gray levels in the reference GSDF described herein, based on an exemplary embodiment of the present invention. In some embodiments, the VDR curve is used to accurately capture the peak contrast sensitivity of human vision over a high dynamic range of luminance values.

[0062] As shown in Figure 2, peak contrast sensitivity does not appear at a fixed spatial frequency value, but rather at smaller spatial frequencies as the light adaptation level (luminance value) decreases. This means that techniques with fixed spatial frequencies (e.g., DICOM) may significantly underestimate the contrast sensitivity of human vision at dim light adaptation levels (low luminance values). Lower contrast sensitivity leads to a higher contrast threshold, resulting in larger quantization increments in the quantized luminance value.

[0063] Unlike the Digital Imaging and Communications in Medicine (DICOM) standard, the VDR curve under the technique described in this paper does not fix the spatial frequency model parameters to a fixed value, such as every four cycles. Rather, the VDR curve changes with spatial frequency, accurately capturing the peak contrast sensitivity of human vision at multiple light adaptation levels. The VDR curve helps to generate a high-precision reference GSDF by appropriately taking into account the sharpening effect resulting from the adaptability of human vision to a wide range of light adaptation levels. Here, the term "high precision" means that perceptual errors resulting from the quantization of luminance values ​​are eliminated or substantially mitigated based on a reference GSDF that best and efficiently captures the nonlinearity of human vision within the constraints of a fixed-size code space (e.g., one of 10 bits, 12 bits, etc.).

[0064] A calculation process may be used to calculate the various gray levels in the reference GSDF (e.g., Table 1). In one exemplary embodiment, the calculation process is sequential, iterative, or recursive, and the contrast threshold (or modulation threshold, e.g., m in equation (4)) is derived from the VDR curve. t The contrast threshold is determined iteratively and applied to obtain a set of gray levels in the reference GSDF. This calculation process may be implemented using the following equation (7).

[0065]

number

[0066] Contrast threshold m related to JND t L may be defined as a relative quantity. For example, L max and L min The difference is L max or L min or L max and L min The middle (for example, L max and L min It is obtained by dividing the average of by one of the specific luminance values. In some embodiments, m t is L max and L min The difference is L max or L min or L max and L min It is obtained by dividing by a multiplier (for example, 2) of any specific luminance value in the middle of the range. When quantizing the luminance value in GSDF into multiple gray levels, L max and L min This may refer to adjacent gray levels in those multiple gray levels. As a result, L j As shown in equation (7), m t L j-1 and L j+1 It may be related to this.

[0067] In alternative embodiments, instead of the linear expression as shown in Equation (7), a non-linear expression may be used to relate the JND or contrast threshold to the gray level. For example, instead of the simple ratio for the contrast threshold shown above, an alternative expression based on dividing the standard deviation by the mean may be used.

[0068] In some embodiments, the reference GSDF covers a range from 0 to 12,000 cd / m with digital code values represented as 12-bit integer values. 2 To further improve the accuracy of the reference GSDF, it may be multiplied by a fraction value f. Further, note that the central digital value L2048 (the digital code value is limited to 0 and 4096 at least as in a 12-bit signed space compatible with SDI) may be mapped to 100 cd / m. Equation (7) may give the following Equation (8). t に割合値(fraction value)fが乗算されてもよい。さらに、中央デジタル値L2048(デジタル符号値は少なくとも、SDIと互換な12ビット符号空間におけるように0および4096に限られることを注意しておく)が100cd / m 2 にマッピングされてもよいです。式(7)は次の式(8)を与えうる。

[0069]

Equation

[0070] Figure 3 shows an exemplary GSDF that maps between multiple gray levels (in logarithmic luminance values) and multiple digital code values in a 12-bit code space, based on an exemplary embodiment of the present invention.

[0071] Figure 4 shows a curve plotting the Weber ratio (delta L / L or ΔL / L) based on the gray level of the exemplary GSDF in Figure 3. The perceptual nonlinearity of human vision shown in Figure 4 is expressed as a function of luminance values ​​on the logarithmic luminance axis. Comparable visual differences in human vision (e.g., JND) correspond to larger delta L / L values ​​at lower luminance values. The curve of the Weber ratio asymptotically approaches a constant value for high luminance values ​​(e.g., a Weber ratio of 0.002 at which Weber's law is satisfied at higher luminance values).

[0072] <7. Functional Models> To obtain a mapping between digital code values ​​and gray levels in the GSDF described in this paper (reference GSDF or device-specific GSDF), one or more analytical functions may be used. These analytical functions may be proprietary, standard-based, or expressions derived from standard-based functions. In some embodiments, a GSDF generator (e.g., 504 in Figure 5) may generate a GSDF in the form of one or more forward lookup tables (LUTs) and / or one or more inverse LUTs based on the one or more analytical functions (or formulas). At least some of these LUTs may be provided to a variety of image data codecs (e.g., 506 in Figure 5) or a wide variety of display devices for use in converting between reference gray levels and reference digital code levels for the purpose of encoding reference image data. Additionally, optionally, or alternatively, at least some of the analytical functions (whose coefficients are integer or floating-point representations) may be provided directly to an image data codec or a wide variety of display devices for use in obtaining a mapping between digital code values ​​and gray levels in the GSDF described herein and / or in performing conversions between gray levels and digital code levels for the purpose of encoding image data.

[0073] In some embodiments, the analytical functions described herein have a forward function that can be used to predict a digital code value based on the corresponding gray level, as follows:

[0074]

number

[0075] In some embodiments, the analytical function is the inverse function corresponding to the forward function in equation (9), and may be used to predict the luminance value based on the corresponding digital sign value, as shown in the following equation.

[0076]

number

[0077] Similarly, luminance values ​​predicted based on multiple digital code values ​​using equation (10) may be compared to observed luminance values. Observed luminance values ​​may be generated using numerical calculations based on the CSF model discussed earlier, or using experimental data of human vision, but are not limited to these methods. In one embodiment, the deviation between the predicted luminance values ​​and the observed luminance values ​​may be derived as a function of parameters n, m, c1, c2, and c3, and this deviation may be minimized in order to derive optimal values ​​for the parameters n, m, c1, c2, and c3 in equation (10).

[0078] The optimal set of values ​​for parameters n, m, c1, c2, and c3 determined using equation (9) may not be the same as the optimal set of values ​​for parameters n, m, c1, c2, and c3 determined using equation (10). If there is a difference between the two sets, one or both of the two sets may be used to generate a mapping between the digital code value and the luminance value. In some embodiments, if the two sets of optimal values ​​for parameters n, m, c1, c2, and c3 are different, they may be harmonized based on minimizing the round-trip error introduced by performing forward and deverse coding operations using both equations (9) and (10). In some embodiments, multiple round trips may be performed to examine the resulting errors in the digital code value and / or luminance value or gray level. In some embodiments, the selection of parameters in equations (9) and (10) may be at least in part based on the criterion that no significant error occurs in one, two, or more round trips. Significant round-trip errors may include, but are not limited to, errors smaller than 0.0001%, 0.001%, 0.01%, 0.1%, 1%, 2%, or other configurable values.

[0079] The embodiments include using a code space of one bit length from one or more different bit lengths to represent digital control values. Optimized values ​​of the parameters in equations (9) and (10) may be obtained for each of a plurality of code spaces, where each code space has a different bit length from one or more different bit lengths. Based on the optimized values ​​of equations (9) and (10), the distribution of sign errors (e.g., forward conversion error, inverse conversion error, or round-trip error in the digital code values ​​based on equations (9) and (10)) may be determined. In some embodiments, a numerical difference of 1 in two digital code values ​​corresponds to a contrast threshold in the optical level between two luminance values ​​represented by those two digital code values ​​(or corresponds to JND). Figure 10A shows the maximum sign error in JND units for a plurality of code spaces, each having one or more different precisions (with different bit lengths), based on some exemplary embodiments. For example, based on the function model described herein, the maximum sign error for an infinite or unlimited bit length code space is 11.252. In contrast, based on the function model described in this paper, the maximum sign error for a 12-bit (or 4096) code space is 11.298. This indicates that a 12-bit code space for digital sign values ​​is a good choice for the function model represented by equations (9) and (10).

[0080] Figure 10B shows the distribution of sign errors for a 12-bit (or 4096) code space by a forward conversion (from luminance value to digital sign value) specified by equation (9), based on an exemplary embodiment. Figure 10C shows the distribution of sign errors for a 12-bit (or 4096) code space by a back conversion (from digital sign value to luminance value) specified by equation (10), based on an exemplary embodiment. Both Figures 10B and 10C show a maximum sign error less than 12.5.

[0081] Figure 11 shows parameter values ​​that may be used in equations (9) and (10) based on an exemplary embodiment. In some embodiments, integer-based formulas are used to represent / approximate these non-integer values ​​in specific implementations of the function models described herein, as shown in the figure. In some other embodiments, fixed-point or floating-point values ​​with one or more precisions (e.g., 14-, 16-, or 32 bits) may be used to represent / approximate these non-integer values ​​in specific implementations of the function models described herein.

[0082] The embodiments include using a function model based on formulas other than those given in equations (9) and (10) (which may be tone-mapping curves). For example, a cone model based on the Naka-Rushton formula, as shown below, may be used in the function models described in this paper.

[0083]

number

[0084] In another example, the function model may be generated using the following raised mu formula:

[0085]

number

[0086] In the context of this paper, in some embodiments, the function model may be used to predict the sign value from the luminance value, or to predict the luminance value from the sign value. The formulas used by the function model may be reversible. The same or similar processing logic may be implemented to perform forward and inverse transformations between these values. In some embodiments, model parameters, including but not limited to exponents, may be expressed by fixed-point values ​​or integer-based formulas. Thus, at least part of the processing logic can be efficiently implemented in hardware only, software only, or a combination of hardware and software. Similarly, at least part of the LUT generated by the function model or model formulas (e.g., equations (9) through (12)) can be efficiently implemented in hardware only, software only, or a combination of hardware and software (including ASICs or FPGAs). In some embodiments, one, two, or more function models may be implemented in a single computing device, a configuration of multiple computing devices, a server, etc. In some embodiments, the predicted sign value error may be within 14 sign values ​​of the target or observed value across the full range of the visible dynamic range of the luminance value. In some embodiments, this holds true for both forward and inverse transformations. The same or different sets of model parameters may be used in the forward and inverse transformations. Round-trip accuracy can be maximized with the optimal values ​​of the model parameters. Various code spaces can be used. In individual embodiments, a 12-bit (4096) code space may be used to accept digital code values ​​with minimum sign error across the full range of the visible dynamic range.

[0087] In the context of this paper, a reference GSDF may refer to a GSDF containing a reference digital code value and a reference gray level that are related under a functional model (whose model parameters may be determined under the CSF model with target or observed values) that is determined numerically based on a CSF model (for example, without determining any functional representation of the mapping between digital code values ​​and luminance values), or determined from data from human vision studies. In some embodiments, the device GSDF may also have a mapping between digital code values ​​and gray levels that can be analytically represented by the functional model described in this paper.

[0088] <8. Exchange of image data based on the reference GSDF> For illustrative purposes, digital code values ​​have been described as residing in a 12-bit code space. However, the present invention is not limited thereto. Digital code values ​​with different code spaces (e.g., bit depths different from 12 bits) may be used in the reference GSDF. For example, a 10-bit integer value may be used to represent a digital code. In a 12-bit representation of a digital code, the digital code value 4076 has a luminance value of 12000 cd / m². 2 Instead of mapping it to a luminance value of 12000 cd / m², the 10-bit representation of the digital code corresponds to a digital code value of 1019. 2 These can be mapped to the following. Thus, these and other variations in the code space (bit depth) can be used for digital code values ​​in the reference GSDF.

[0089] A reference GSDF may be used to exchange image data across different GSDFs, which may be individually designed for each type of image acquisition or image rendering device. For example, a GSDF implemented with a particular type of image acquisition or image rendering device may implicitly or explicitly depend on model parameters that do not match those of a standard GSDF or a device-specific GSDF in another type of image acquisition or image rendering device.

[0090] The reference GSDF may correspond to the curved shapes depicted in Figures 3 and 4. Generally, the shape of a GSDF depends on the parameters used to derive or design the GSDF. Therefore, the reference GSDF depends on the reference CSF model and the reference model parameters used to generate the reference GSDF from the reference CSF model. The curved shape of a device-specific GSDF depends on the specific device, and if that device is a display, it includes display parameters and viewing conditions.

[0091] For example, the supported luminance value range is 500 cd / m². 2 Displays limited to below a certain luminance may not experience the increased slope in the high-luminance region, as shown in Figure 3 (this occurs when human vision shifts to logarithmic behavior across all frequencies). Driving such a display with the curve shape shown in Figure 3 can lead to suboptimal (e.g., worse than optimal) gray level allocation, where too much gray level is allocated to bright areas and too little to dark areas.

[0092] In another example, a low-contrast display is designed for outdoor use under varying daylight conditions. The luminance range of this display may appear almost entirely, or nearly entirely, in the logarithmic behavior region of Figure 3. Driving this low-contrast display with the curve shape of Figure 3 can also lead to suboptimal (worse than optimal) gray level allocation, with too much gray level allocated to dark areas and insufficient gray level allocated to bright areas.

[0093] Under the techniques described in this paper, each display may use its own unique GSDF (which depends not only on display parameters but also on viewing conditions that affect, for example, the actual black level) to optimally support perceptual information in image data encoded with a reference GSDF. The reference GSDF is used by one or more upstream (e.g., encoding) devices for the overall encoding of the image data in order to preserve as much perceptual detail as possible. The image data encoded in the reference GSDF is then delivered to one or more downstream (e.g., decoding) devices. In one exemplary embodiment, the encoding of image data based on the reference GSDF is independent of the specific device that subsequently decodes and / or renders the image data.

[0094] Each device (e.g., a display) has a unique GSDF that supports / optimizes device-specific gray levels. These device-specific gray levels may be known to the display manufacturer, or they may be individually designed by the manufacturer to support a device-specific GSDF (which may or may not be standard-based). The device's line driver may be implemented with device-specific quantized luminance values. For the device, optimization may best be done based on the device-specific quantized luminance values. Furthermore, a dark black level (e.g., the lowest device-specific gray level) that can be used as a lower limit of the device-specific gray level range may be set in part based on the current ambient light level and / or the device's light reflectivity (which may be known to the manufacturer). Once the dark black level is set in this way, the device-specific gray levels may be obtained or set by implicitly or explicitly accumulating (e.g., integrating) the quantization steps in the device's line driver. Gray level derivation and / or adjustment may or may not be done at runtime while the device is rendering images concurrently.

[0095] Thus, under the techniques described in this paper, embodiments of the present invention may include, but are not limited to, encoding image data using a reference GSDF and decoding and rendering said image data using a display-specific GSDF.

[0096] The techniques described in this paper may be used to exchange image data across a variety of devices having different GSDFs. Figure 5 shows an exemplary framework (500) for exchanging image data with devices having different GSDFs, based on an exemplary embodiment of the present invention. As shown in Figure 5, an adaptive CSF model (502) may be used to generate a reference GSDF (504). The term “adaptive” may mean that the CSF model is able to adapt to the nonlinearity and behavior of human vision. The adaptive CSF model may be constructed, at least in part, on a plurality of CSF parameters (or model parameters). These plurality of model parameters include, for example, a light adaptive level, a display area in degrees of width, a noise level, perspective accommodation (physical viewing distance), luminance, or a color modulation vector (which may relate, for example, to a test image or image pattern used in the adaptive CSF model (502)).

[0097] An upstream (e.g., encoding) device may receive image data to be encoded using the reference GSDF(504) before the image data or its derivatives are transmitted or delivered to a downstream (e.g., decoding) device. The image data to be encoded may initially be in any of several formats (standard-based, proprietary, extensions thereof, etc.) and / or may be derived from any of several image sources (camera, image server, physical medium, etc.). Examples of image data to be encoded include, but are not limited to, raw or other high-bit-depth images(s)530. Such raw or high-bit-depth images may originate from a camera, studio system, art director system, another upstream image processing system, image server, content database, etc. The image data may include, but are not limited to, digital photographs, video image frames, 3D images, non-3D images, computer-generated graphics, etc. The image data may include scene-referenced images, device-referenced images, or images with varying dynamic ranges. Examples of image data to be encoded may include high-quality versions of the original images that are to be edited, downsampled, and / or compressed, along with metadata, into an encoded bitstream for delivery to an image receiving system (a downstream image processing system such as displays from various manufacturers). Raw or other high-bit-depth images may be those with high sampling rates used by professionals, art studios, broadcasters, high-end media production entities, etc. The image data to be encoded may be computer-generated, entirely or partially, or even obtained, entirely or partially, from existing image sources such as old films and documentaries.

[0098] In the context of this paper, the phrase “image data to be encoded” may refer to the image data of one or more images. The image data to be encoded may include floating-point or fixed-point image data and may be in any color space. In one exemplary embodiment, the one or more images may be in the RGB color space. In another exemplary embodiment, the one or more images may be in the YUV color space. In one example, each pixel in the images described herein has floating-point pixel values ​​for all channels defined in that color space (e.g., red, green, and blue color channels in the RGB color space). In another example, each pixel in the images described herein has fixed-point pixel values ​​for all channels defined in that color space (e.g., 16-bit or more / fewer-bit fixed-point pixel values ​​for the red, green, and blue color channels in the RGB color space). Each pixel may optionally and / or alternatively have downsampled pixel values ​​for one or more channels in the color space.

[0099] In some embodiments, in response to receiving image data to be encoded, an upstream device in the framework (500) maps luminance values ​​specified by or determined from the image data to reference digital code values ​​in a reference GSDF, and generates reference-encoded image data encoded using the reference digital code values ​​based on the image data to be encoded. The mapping operation from luminance values ​​to reference digital code values ​​based on the image data to be encoded may include selecting a reference digital code value such that the corresponding reference gray level (e.g., as shown in Table 1) closely matches or approximates the luminance value specified by or determined from the image data to be encoded, compared to any other reference luminance value in the reference GSDF, and replacing the luminance value with the reference digital code value in the reference-encoded image data.

[0100] Additional, optional, or alternative, pre-processing and post-processing steps (which may include, but are not limited to, color space conversion, downsampling, upsampling, tone mapping, color grading, decompression, compression, etc.) may be performed as part of generating the reference-encoded image data.

[0101] In one exemplary embodiment, the framework (500) may have software and / or hardware components (e.g., an encoding or formatting unit (506)) configured to encode and / or format the reference encoded image data into one or more encoded bitstreams or image files. The encoded bitstreams or image files may be in a standards-based format, a proprietary format, or an extended format at least partially based on a standards-based format. Additionally and / or optionally, the encoded bitstreams or image files may have metadata including one or more reference GSDFs used to generate the reference encoded image data, relational parameters related to pre-processing or post-processing (e.g., model parameters; such as minimum luminance value, maximum luminance value, minimum digital code value, maximum digital code value as shown in Table 1, Figures 3 and 4; identification fields for identifying a particular CSF among several CSFs; and reference browsing distance).

[0102] In some embodiments, the framework (500) may have one or more separate upstream devices. For example, at least one of the one or more upstream devices in the framework (500) may be configured to encode image data based on a reference GSDF. The upstream devices may have software and / or hardware components configured to perform functions related to 502, 504 and 506 in Figure 5. The encoded bitstream or image file may be output by the upstream devices (502, 504 and 506 in Figure 5) through a network connection, a digital interface, a physical storage medium, etc., and delivered in an image data flow (508) to other image processing devices for processing or rendering.

[0103] In some exemplary embodiments, the framework (500) further comprises one or more downstream devices as one or more separate devices. The downstream devices may be configured to receive / access encoded bitstreams or image files output by the one or more upstream devices from the image data flow (508). For example, the downstream devices may have software and / or hardware components (e.g., a decoding or reformatting unit (510)) configured to decode and / or reformatize the encoded bitstream or image file and recover / retrieve the reference encoded image data therein. As shown in Figure 5, the downstream devices may have a variety of display devices.

[0104] In some embodiments, a display device (not shown) may be designed and / or implemented to support a reference GSDF. If the display device supports all gray levels in the reference GSDF, high-precision HDR image rendering may be provided. The display device may render images at a level of detail that is finer than or equal to that detectable by human vision.

[0105] In some embodiments, the native digital code value of the display device in the device-specific GSDF (which may be implemented as a digitized voltage value in the display system, such as a digital drive level or DDL) may correspond to a device-specific gray level (or luminance value) that is different from that of the reference GSDF. The device-specific gray levels may be designed to support other standards, including those using sRGB, Rec. 709, or representations related to complementary densities. Additionally, optionally, or alternatively, the device-specific gray levels may be based on the inherent DAC characteristics of the display drive.

[0106] In some embodiments, display device A (512-A) may be designed and / or implemented to support a device-specific GSDF A (514-A) of a visible dynamic range (VDR) display. GSDF A (514-A) may be based on a 12-bit bit depth (12-bit code space) for the device-specific digital code value, a contrast ratio (CR) of 10,000:1, and a >P3 color gamut. GSDF A (514-A) may support gray levels within a first partial range (e.g., from 0 to 5,000 cd / m 2 ) within the entire range of the reference GSDF (504). Alternatively or optionally, GSDF A (514-A) may support the entire range within the reference GSDF (504) (e.g., from 0 to 12,000 cd / m 2 ), but may have fewer reference gray levels than all of the reference GSDF (504).

[0107] In some embodiments, the display device B(512-B) may be designed and / or implemented to support an instrument-specific GSDF B(514-B) for a narrower dynamic range than VDR. For example, the display device B(512-B) may be a standard dynamic range (SDR) display. In the usage of this paper, the terms “standard dynamic range” and “low dynamic range” and / or their corresponding abbreviations “SDR” and “LDR” may be used synonymously and / or interchangeably. In some embodiments, the GSDF B(514-B) may support an 8-bit bit depth for instrument-specific digital code values, a contrast ratio (CR) of 500–5,000:1, and a color range as defined in Rec. 709. In some embodiments, the GSDF B(514-B) supports a second subrange of reference GSDF(504) (e.g., 0 to 2,000 cd / m²). 2 You may provide gray levels within the parentheses.

[0108] In some embodiments, the display device C(512-C) may be designed and / or implemented to support an instrument-specific GSDF C(514-C) for an even narrower dynamic range than SDR. For example, the display device C(512-C) may be a tablet display. In some embodiments, the GSDF C(514-C) may support an 8-bit bit depth for instrument-specific digital code values, a contrast ratio (CR) of 100-800:1, and a color range smaller than that defined in Rec.709. In some embodiments, the GSDF C(514-C) may support a third subrange of reference GSDF(504) (e.g., 0 to 1,200 cd / m²). 2 You may provide gray levels within the parentheses.

[0109] In some embodiments, a display device (e.g., display device D(512-D)) may be designed and / or implemented to support a device-specific GSDF (e.g., GSDF D(514-D)) for a very limited dynamic range much narrower than that of an SDR. For example, display device D(512-D) may be an electronic paper display. In some embodiments, GSDF D(514-D) may support a bit depth of 6 bits or less for device-specific digital code values, a contrast ratio (CR) of 10:1 or less, and a color range much smaller than that defined in Rec.709. In some embodiments, GSDF D(514-D) may support a fourth subrange of reference GSDF(504) (e.g., 0 to 100 cd / m²). 2 It may also support gray levels within the parentheses.

[0110] The accuracy in image rendering can be gracefully scaled down for each of the display devices A through D (512-A through -D). In some embodiments, a subset of gray levels in each of the device-specific GSDFs A through D (514-A through -D) may be correlated or mapped to supported reference gray levels in a reference GSDF (504) in such a way that perceptually perceptible errors in the range of gray levels supported by the display device are evenly distributed.

[0111] In some embodiments, a display device (e.g., one of 512-A to -D) having a device-specific GSDF (e.g., one of 514-A to -D) receives / extracts reference-encoded image data encoded based on the reference GSDF. In response, the display device or a conversion unit within it (one of 516-A to -D) maps the reference digital code values ​​specified in the reference-encoded image data to device-specific digital code values ​​native to the display device. This may be performed in one of several ways. In one example, the mapping from reference digital code values ​​to device-specific digital code values ​​involves selecting a device-specific gray level (corresponding to a device-specific digital code value) that closely matches or approximates the reference gray level (corresponding to a reference digital code value) compared to any other device-specific gray level. In another example, the mapping from a reference digital code value to an instrument-specific digital code value involves (1) determining a tone-mapped luminance value based on a reference gray level associated with a reference GSDF (corresponding to the reference digital code value), and (2) selecting an instrument-specific gray level (corresponding to the instrument-specific digital code value) that closely matches or approximates the tone-mapped luminance value compared to any other instrument-specific gray level.

[0112] Subsequently, the display device or a driver chip within it (one of 518-A to -D) may use a display-specific digital code value to render an image using a device-specific gray level corresponding to a display-specific code value.

[0113] Generally, a reference GSDF may be based on a different CSF model than the one on which the display-specific GSDF is based. Conversion / mapping between the reference GSDF and the device-specific GSDF is necessary. Even if the same CSF model is used to generate both the reference GSDF and the device-specific GSDF, different values ​​for model parameters may have been used in deriving those GSDFs. For the reference GSDF, the model parameter values ​​may be set conservatively to preserve details for a wide variety of downstream devices, while for the device-specific GSDF, the model parameter values ​​may reflect the specific design / implementation and the viewing conditions under which the display device renders images. Since the viewing condition parameters of a particular display device (e.g., ambient light level, display device light reflectivity, etc.) differ from the model parameter values ​​used to derive the reference GSDF, conversion / mapping between the reference GSDF and the device-specific GSDF is still necessary. Here, the viewing condition parameters may include those that affect display quality (e.g., contrast ratio) and those that increase the black level (e.g., minimum gray level, etc.). The conversion / mapping between a reference GSDF and an instrument-specific GSDF using the techniques described in this paper improves the quality of image rendering (for example, by increasing luminance values ​​in high-value regions to improve contrast ratio).

[0114] <9. Converting Reference Encoded Image Data> Figure 6 shows an exemplary conversion unit (e.g., 516) based on several embodiments of the present invention. The conversion unit (516) may be one of several conversion units (e.g., 516-A to -D) shown in Figure 5 (e.g., 516-A), but is not limited thereto. In some embodiments, the conversion unit (516) may receive first definition data for a reference GSDF (REF GSDF) and second definition data for a device-specific GSDF (e.g., GSDF-A (514-A in Figure 5)). As used herein, “device-specific” and “display-specific” may be used interchangeably when the device is a display.

[0115] Based on the received definition data, the conversion unit (516) cascades the reference GSDF with the display-specific GSDF to form a conversion lookup table (conversion LUT). The cascade between the two GSDFs may include comparing the gray levels in the two GSDFs and, based on the results of the gray level comparison, establishing a mapping between the reference digital code value in the reference GSDF and the display-specific digital code value in the display-specific GSDF.

[0116] More specifically, given a reference digital code value in a reference GSDF, the corresponding reference gray level may be determined based on the reference GSDF. The reference gray level thus determined may be used to locate a device-specific gray level in a display-specific GSDF. In one exemplary embodiment, the locable device-specific gray level may closely match or approximate the reference gray level to any other display-specific gray level in the display-specific GSDF. In another exemplary embodiment, a tone-mapped luminance value may be obtained by a global or local tone-mapping operator acting on the reference gray level, and the locable device-specific gray level may closely match or approximate the tone-mapped luminance value to any other display-specific gray level in the display-specific GSDF.

[0117] Using a device-specific gray level, the corresponding display-specific digital code value may be identified from the display-specific GSDF. In the conversion LUT, an entry consisting of a reference digital code value and a display-specific code value may be added or defined.

[0118] Each of the above steps may be repeated for other reference digital code values ​​in the reference GSDF.

[0119] In some embodiments, the transformation LUT may be pre-built and stored before the image data to be processed based on the transformation LUT is received and processed. In alternative embodiments, the image data to be processed using the transformation LUT may be analyzed. The results of the analysis may be used to set up, or at least adjust, the correspondence between reference digital code values ​​and device-specific digital code values. For example, if the image data shows a particular concentration or distribution of luminance values, the transformation LUT may be set up to store a large amount of detail in the regions where luminance values ​​are concentrated.

[0120] In some embodiments, the conversion unit (516) has one or more software and / or hardware components (comparison subunit (602)) configured to compare the quantization step (e.g., the difference in luminance values ​​or ΔL between adjacent digital code values) in both the reference GSDF and the display-specific GSDF (514-A). For example, the quantization step in a reference digital code value in the reference GSDF may be a difference in reference luminance values ​​(reference GSDF ΔL), while the quantization step in a display-specific digital code value in the display-specific GSDF may be a difference in display-specific luminance values ​​(display-specific GSDF ΔL), where the display-specific digital code value corresponds to (or pairs with) the reference digital code value in the conversion LUT unit. In some embodiments, the comparison subunit (602) compares these two luminance value differences. This process is essentially a test that can be performed based on the ΔL value, or arbitrarily and / or alternatively based on the relative slopes of the two GSDF curves.

[0121] The quantization step for luminance values ​​in the display-specific GSDF is typically larger than that of the reference GSDF. This is because one or more reference gray levels from the reference GSDF (e.g., corresponding to high bit depth regions) are merged with display-specific gray levels from the display-specific GSDF (e.g., corresponding to low bit depth regions). In such cases, dithering is used to remove band-generated artifacts. As part of the overall dithering, dithering is also performed on local surrounding output pixels (in space and / or time). In a sense, the human eye can be represented as a low-pass filter. At least in this sense, averaging local surrounding pixels as described in this paper produces the desired output gray level, which reduces and / or eliminates band-generated visual artifacts that would normally be present due to the large quantization step in the display-specific GSDF.

[0122] In less common cases, the quantization step for luminance values ​​for a reference GSDF may sometimes be larger than that of a display-specific GSDF. A process based on decontouring algorithms is used to synthesize the output gray level based on the input gray level, for example, by averaging neighboring input pixels.

[0123] Correspondingly, in the case of the "Y" path in Figure 6, where the reference GSDF ΔL is greater than the display-specific GSDF ΔL, the contour removal algorithm flag is set for the entry in the transform LUT that has the reference digital code value and the display-specific digital code value.

[0124] In the case of the "N" path in Figure 6, where the reference GSDF ΔL is smaller than the display-specific GSDF ΔL, the dither algorithm flag is set for the entry in the conversion LUT that has the reference digital code value and the display-specific digital code value.

[0125] If the reference GSDF ΔL is equal to the display-specific GSDF ΔL, neither the contour removal algorithm flag nor the dithering algorithm flag is set for the entry in the conversion LUT that has the reference digital code value and the display-specific digital code value.

[0126] The contour removal and dithering algorithm flags may be stored in the transform LUT along with the entries, or they may be stored in a related data structure outside the transform LUT but operationally linked to it.

[0127] In some embodiments, the conversion unit (516) receives reference-encoded image data, which may be in the form of a high-bit-depth or floating-point input image, and is configured to map the reference digital code values ​​specified in the reference GSDF to the display-specific digital code values ​​specified in the display-specific GSDF. In addition to mapping digital code values ​​between those GSDFs, the conversion unit (516) may be configured to perform edge removal or dithering based on the setting of the algorithm flags discussed earlier (edge ​​removal algorithm flag or dithering algorithm flag).

[0128] As mentioned earlier, the reference GSDF is likely to contain more detail than the display-specific GSDF. Therefore, the "Y" path in Figure 6 may not occur, or may occur less frequently. In some embodiments, the "Y" path and related processing may be omitted to simplify the implementation of the conversion unit.

[0129] In some embodiments, given a reference digital code value determined for a pixel in the reference-encoded image data, the conversion unit (516) searches for the corresponding display-specific digital code value in the conversion LUT and replaces the reference digital code value with the corresponding display-specific digital code value. Additionally and / or optionally, the conversion unit (516) determines whether a decontamination or dithering algorithm should be performed on the pixel, based on the presence / setting of an algorithm flag for an entry in the conversion LUT containing the reference digital code value and the display-specific digital code value.

[0130] If it is determined that neither the edge removal algorithm nor the dithering algorithm should be executed (i.e., there are no instructions or flags to execute either algorithm), then edge removal and dithering will not be performed on that pixel for the time being.

[0131] If it is determined that a decontour algorithm should be executed, the transformation unit (516) may execute one or more decontour algorithms. Executing the one or more decontour algorithms may include receiving image data of local neighboring pixels of the input and inputting the image data of said local neighboring pixels into the decontour algorithm.

[0132] If it is determined that a dithering algorithm should be executed, the transformation unit (516) may execute one or more dithering algorithms.

[0133] The pixel may still be subject to de-edge treatment or dithering if the conversion unit (516) determines that de-edge treatment or dithering should be performed with respect to neighboring pixels. In one example, the device-specific (output) gray level of the pixel may be used to dither local neighboring pixels. In another example, the reference (input) gray level of the pixel may be used to de-edge local neighboring pixels.

[0134] In some embodiments, the conversion unit (516) outputs the processing results of the above steps to a downstream processing unit or subunit. The processing results include display-specific encoded image data in a display-specific bit depth output image format encoded with digital code values ​​in a display-specific GSDF (e.g., GSDF-A).

[0135] Figure 7 shows an exemplary SDR display (700) implementing 8-bit image processing. The SDR display (700) or a VDR decoding unit within it receives an encoded input. The encoded input contains reference-encoded image data within an image data container, which may be one of several image data container formats. The VDR decoding unit (702) decodes the encoded input and determines / retrieves the reference-encoded image data from it. The reference-encoded image data may contain image data for individual pixels in a color space (e.g., RGB color space, YCbCr color space, etc.). The image data for individual pixels may be encoded with a reference digital code value in a reference GSDF.

[0136] Additionally and / or optionally, the SDR display (700) has a display management unit (704) that maintains display parameters for the SDR display (700). The display parameters may define, at least in part, a display-specific GSDF (e.g., GSDF-B in Figure 5) associated with the SDR display (700). The display parameters defining the display-specific GSDF may include the maximum (max) and minimum (min) gray levels supported by the SDR display (700). The display parameters may also include the primary colors supported by the SDR display, the display size, the light reflectivity of the image rendering surface of the SDR display, and the ambient light level. Some of the display parameters may be pre-configured with fixed values. Some of the display parameters may be measured by the SDR display (700) in real time or near real time. Some of the display parameters may be configurable by the user of the SDR display (700). Some of the display parameters may be pre-configured with default values ​​and may be measured or overridden by the user. The display management unit (704) establishes / shapes the perceptual nonlinearity of display-specific gray levels based on the reference GSDF and may additionally and / or optionally perform tone mapping as part of establishing / shaping the display-specific gray levels. For example, for the purpose of establishing / shaping the perceptual nonlinearity of display-specific gray levels based on the reference GSDF, a transformation LUT and / or other related metadata (e.g., dithering and de-edge processing flags) as shown in Figure 5 may be established by the display management unit (704). The cascading of the processing described above may be implemented using the display management unit (704) to generate other relational metadata (712) related to one or both of the transformation LUT and / or the reference GSDF and the display-specific GSDF.The conversion LUT and / or other related metadata (712) may be accessed and used by other units or subunits within the SDR display (700). Furthermore, the conversion LUT and / or other related metadata may be used as metadata (714) for inverting perceptual nonlinearity, or for deriving such metadata. In the usage of this paper, inverting perceptual nonlinearity may include converting display-specific digital sign values ​​to display-specific digital drive levels (e.g., digitized voltage levels in the display device).

[0137] Additionally and / or optionally, the SDR display (700) includes a conversion unit (516) as shown in Figures 5 and 6, and an 8-bit perceptual quantizer (706). In some embodiments, the SDR display (700) or the conversion unit (516) and the 8-bit perceptual quantizer (706) within it convert reference-encoded image data into a display-specific bit-depth output image encoded with a display-specific digital code value associated with a display-specific GSDF (e.g., GSDF-A or GSDF-B in Figure 5), and quantizes the display-specific bit-depth output image into perceptually encoded image data in an 8-bit code space. As used in this paper, the term “perceptually encoded” may refer to a type of encoding based on a perceptual model of human vision, such as the CSF on which the reference GSDF is based.

[0138] Additionally and / or optionally, the SDR display (700) may have a video post-processing unit (708) that can perform zero, one, or more image processing operations on perceptually encoded image data in an 8-bit luminance representation. These image processing operations may include, but are not limited to, compression, decompression, color space conversion, downsampling, upsampling, or color grading. The results of these operations may be output to other parts of the SDR display (700).

[0139] In one exemplary embodiment, the SDR display (700) has an 8-bit inverse perceptual quantizer (710) configured to convert a display-specific digital code value resulting from an image processing operation into a display-specific digital drive level (e.g., a digitized voltage level). The display-specific digital drive level generated by (or converted back from) the digital code value by the inverse perceptual quantizer (710) may, in particular, support one of several types of luminance nonlinearities that can be supported in the SDR display (700). In one example, the inverse perceptual quantizer (710) converts the display-specific digital code value into a display-specific digital drive level that supports a luminance nonlinearity related to Rec. 709. In another example, the inverse perceptual quantizer (710) converts the display-specific digital code value into a display-specific digital drive level that supports a luminance nonlinearity related to the linear luminance region or the logarithmic luminance region (which may be relatively easy to integrate with local dimming operations). In another example, the inverse perceptual quantizer (710) converts the display-specific digital code value into a display-specific digital drive level that has an optimal arrangement of display-specific gray levels for that particular display (700) and possibly supports a display-specific CSF (or its associated GSDF) that is tuned for the viewing conditions specific to that display (700).

[0140] <10. Exemplary Process Flow> Figure 8A shows an exemplary process flow based on one embodiment of the present invention. In some embodiments, one or more computing devices or components, such as one or more computing devices within a framework (500), may perform this process flow. In block 802, a computing device receives image data to be encoded.

[0141] In block 804, the computing device encodes the image data to be encoded based on a reference mapping between a set of reference digital code values ​​and a set of reference gray levels to obtain reference-encoded image data. Here, the luminance values ​​in the image data to be encoded are represented by the set of reference digital code values. The luminance difference between two reference gray levels represented by two adjacent reference digital code values ​​in the set of reference digital code values ​​may be inversely proportional to the peak contrast sensitivity of human vision adapted to a particular light level.

[0142] In block 806, the computing device outputs reference-encoded image data.

[0143] In one embodiment, the computing device determines a reference grayscale display function (GSDF) based on a contrast sensitivity function (CSF). The reference GSDF specifies a reference mapping between a set of reference digital code values ​​and a set of reference gray levels. The CSF model includes one or more model parameters, which may have angular sizes that fall within a range including one or more of the following: between 25°×25° and 30°×30°, between 30°×30° and 35°×35°, between 35°×35° and 40°×40°, between 40°×40° and 45°×45°, or greater than 45°×45°.

[0144] In one embodiment, the computing device derives a number of submappings by assigning intermediate luminance values ​​within the range of luminance values ​​supported by the set of reference gray levels to intermediate digital code values ​​in a code space accommodating the set of reference digital code values, and performing one or more summation or integral calculations. Each submapping maps the reference digital code values ​​in the set of reference digital code values ​​to the reference gray levels in the set of reference gray levels. The intermediate luminance values ​​may be selected from a range including one or more of less than 50 nits, between 50 nits and 100 nits, between 100 nits and 500 nits, or 500 nits or more.

[0145] In one exemplary embodiment, the set of reference gray levels covers a dynamic range with an upper limit having values ​​less than 500 nits, between 500 nits and 1000 nits, between 1000 nits and 5000 nits, between 5000 nits and 10000 nits, between 10000 nits and 15000 nits, or greater than 15000 nits.

[0146] In one embodiment, the peak contrast sensitivity is determined from one of several contrast sensitivity curves determined based on a contrast sensitivity function (CSF) model having model parameters including a luminance value variable, a spatial frequency variable, or one or more of the other variables.

[0147] In one embodiment, at least two peak contrast sensitivities, determined based on at least two of the plurality of contrast sensitivity curves, appear at two different spatial frequency values.

[0148] In one embodiment, the computing device converts one or more input images, which are received, transmitted, or stored, and are represented using image data to be encoded from an input video signal, into one or more output images, which are received, transmitted, or stored, and are represented using reference-encoded image data included in an output video signal.

[0149] In one embodiment, the image data to be encoded includes image data encoded in one of the following: a high-resolution high dynamic range (HDR) image format; the RGB color space associated with the Academy of Motion Picture Arts and Sciences (AMPAS) Academy Color Encoding Specification (ACES); the Digital Cinema Initiative P3 color space standard; the Reference Input Medium Metric / Reference Output Medium Metric (RIMM / ROMM) standard; the sRGB color space; or the RGB color space associated with the International Telecommunication Union (ITU) BT.709 Recommendation standard.

[0150] In one embodiment, the luminance difference between two reference gray levels, represented by two adjacent reference digital code values, is smaller than the minimum known difference threshold at that particular light level.

[0151] In one exemplary embodiment, the particular light level is a luminance value between the two luminance values ​​(including both ends).

[0152] In one embodiment, the set of reference digital code values ​​may be less than 12 bits; between 12 and 14 bits; at least 14 bits; or integer values ​​in a code space with a bit depth of 14 bits or more.

[0153] In one embodiment, the set of reference gray levels may have a set of quantized luminance values.

[0154] Figure 8B shows another exemplary process flow based on one embodiment of the present invention. In some embodiments, one or more computing devices or components, such as one or more computing devices within a framework (500), may perform this process flow. In block 852, the computing device determines a digital code mapping between a set of reference digital code values ​​and a set of device-specific digital code values, where the set of reference digital code values ​​are mapped to a set of reference gray levels in the reference mapping, while the set of device-specific digital code values ​​are mapped to a set of device-specific gray levels in the device-specific mapping.

[0155] In block 854, the computing device receives reference-encoded image data encoded with the set of reference digital code values. The luminance values ​​in the reference-encoded image data are based on the set of reference digital code values. The luminance difference between two reference gray levels represented by two adjacent reference digital code values ​​in the set of reference digital code values ​​may be inversely proportional to the peak contrast sensitivity of human vision adapted to a particular light level.

[0156] In block 856, the computing device transcodes the reference-encoded image data, which is encoded with the set of reference digital code values, into device-specific image data, which is encoded with the device-specific digital control code, based on the digital code mapping. The luminance values ​​in the device-specific image data are based on the set of device-specific digital code values.

[0157] In one embodiment, the computing device determines a set of correspondences between a set of reference digital code values ​​and a set of device-specific digital code values, where the correspondences in the set relate the reference digital code values ​​in the set of reference digital code values ​​to the device-specific digital code values. The computing device further compares a first luminance difference in the reference digital code value with a second luminance difference in the device-specific digital code value and stores an algorithm flag based on the comparison of the first and second luminance differences to determine whether dithering, contour removal, or no operation should be performed on the reference digital code value.

[0158] In one embodiment, the computing device determines a reference digital code value from reference-encoded image data for a pixel, and further determines whether an algorithm flag is set for that reference digital code value. In response to determining that the algorithm flag for edge removal is set, the computing device executes an edge removal algorithm on that pixel. Alternatively, in response to determining that the algorithm flag for dithering is set, the computing device executes a dithering algorithm on that pixel.

[0159] In one embodiment, the computing device renders one or more images on a display based on device-specific image data encoded with a set of device-specific digital control codes. The display may, but may not be, a visible dynamic range (VDR) display, a standard dynamic range (SDR) display, a tablet computer display, or a handheld device display.

[0160] In one embodiment, a device-specific grayscale display function (GSDF) specifies a device-specific mapping between a set of device-specific digital code values ​​and a set of device-specific gray levels.

[0161] In one embodiment, the device-specific mapping is derived based on one or more display parameters and zero or more browsing condition parameters.

[0162] In one embodiment, the set of device-specific gray levels covers a dynamic range with an upper limit having values ​​less than 100 nits, 100 nits or more but less than 500 nits, between 500 nits and 1000 nits, between 1000 nits and 5000 nits, between 5000 nits and 10000 nits, or greater than 10000 nits.

[0163] In one embodiment, the computing device converts one or more input images, which are received, transmitted, or stored, and are represented using reference-encoded image data from an input video signal, into one or more output images, which are received, transmitted, or stored, and are represented using device-specific image data included in an output video signal.

[0164] In one embodiment, device-specific image data supports image rendering in one of the following formats: a high-resolution high dynamic range (HDR) image format, the RGB color space associated with the Academy of Motion Picture Arts and Sciences (AMPAS) Academy Color Encoding Specification (ACES), the Digital Cinema Initiative P3 color space standard, the Reference Input Medium Metric / Reference Output Medium Metric (RIMM / ROMM) standard, the sRGB color space, or the RGB color space associated with the International Telecommunication Union (ITU) BT.709 recommendation standard.

[0165] In one embodiment, the set of device-specific digital code values ​​have integer values ​​in a code space with a bit depth of 8 bits; greater than 8 bits but less than 12 bits; or 12 bits or more.

[0166] In one embodiment, the set of device-specific gray levels may have a set of quantized luminance values.

[0167] In various embodiments, encoders, decoders, systems, etc., perform any or part of the methods described above.

[0168] <11. Implementation Mechanisms - Hardware Overview> In one embodiment, the technique described herein is implemented by one or more special-purpose computing devices. The special-purpose computing device may be fixedly configured to perform the technique, or it may include one or more digital electronic devices persistently programmed to perform the technique, such as one or more application-specific integrated circuits (ASICs) or field-programmable gate arrays (FPGAs), or it may include one or more general-purpose hardware processors programmed to perform the technique according to program instructions in firmware, memory, or other storage or combination. Such a special-purpose computing device may achieve the technique by combining custom fixed-configuration logic, ASICs, or FPGAs with custom programming. The special-purpose computing device may be a desktop computer system, a portable computer system, a handheld device, a networking device, or any other device incorporating fixed configuration and / or programmed logic to implement the technique.

[0169] For example, Figure 9 is a block diagram showing a computer system 900 in which an exemplary embodiment of the present invention may be implemented. The computer system 900 includes a bus 902 or other communication mechanism for communicating information and a hardware processor 904 coupled to the bus 902 for processing information. The hardware processor 904 may be, for example, a general-purpose microprocessor.

[0170] The computer system 900 also includes main memory 906 coupled to bus 902 for storing information and instructions to be executed by processor 904, such as random access memory (RAM) or other dynamic storage devices. Main memory 906 may also be used to store temporary variables or other intermediate information during the execution of instructions to be executed by processor 904. When such instructions are stored in a non-temporary storage medium accessible to processor 904, the computer system 900 becomes a special-purpose machine customized to perform the processing specified in the instructions.

[0171] The computer system 900 further includes a read-only memory (ROM) 908 or other static storage device coupled to the bus 902 for storing static information and instructions for the processor 904. A storage device 910, such as a magnetic disk or optical disk, is provided and coupled to the bus 902 for storing information and instructions.

[0172] The computer system 900 may be coupled via a bus 902 to a display 912, such as a liquid crystal display, for displaying information to the computer user. An input device 914, including alphanumeric and other keys, is coupled to the bus 902 to transmit information and command selections to the processor 904. Another type of user input device is a cursor control 916, such as a mouse, trackball, or cursor directional keys, for transmitting directional information and command selections to the processor 904 and for controlling cursor movement on the display 912. This input device typically has two degrees of freedom in two axes, a first axis (e.g., x) and a second axis (e.g., y), so that the device can specify a position in a plane.

[0173] The computer system 900 may use customized fixed-configuration logic, one or more ASICs or FPGAs, firmware and / or programmed logic that, in combination with the computer system, make or program the computer system 900 a special-purpose machine. According to one embodiment, the technique described herein is executed by the computer system 900 in response to the processor 904 executing one or more sequences of one or more instructions contained in main memory 906. Such instructions may be read into main memory 906 from another storage medium, such as storage device 910. By executing the sequence of instructions contained in main memory 906, the processor 904 performs the process steps described herein. In alternative embodiments, fixed-configuration circuitry may be used instead of or in combination with software instructions.

[0174] As used in this paper, the term “storage medium” refers to any non-temporary medium that stores data and / or instructions that cause a machine to operate in a particular manner. Such storage mediums may include non-volatile and / or volatile media. Non-volatile media include, for example, optical or magnetic disks such as storage device 910. Volatile media include dynamic memory such as main memory 906. Common forms of storage media include, for example, floppy disks, flexible disks, hard disks, semiconductor drives, magnetic tapes or any other magnetic data storage media, CD-ROMs, any other optical data storage media, any physical media with a pattern of holes, RAM, PROMs and EPROMs, flash EPROMs, NVRAMs, and any other memory chips or cartridges.

[0175] A storage medium is distinct from a transmission medium, but may be used in conjunction with a transmission medium. A transmission medium participates in transferring information between storage mediums. For example, a transmission medium includes coaxial cables, copper wires, and optical fibers, and includes wires forming a bus 902. A transmission medium may also take the form of acoustic or optical waves, such as those generated during radio and infrared data communications.

[0176] Various forms of media may be involved in transporting one or more sequences of one or more instructions to the processor 904 for execution. For example, the instructions may initially be carried on a magnetic disk or semiconductor drive of a remote computer. The remote computer can load the instructions into its dynamic memory and send them over a telephone line using a modem. A modem local to computer system 900 can receive data over the telephone line and convert that data into an infrared signal using an infrared transmitter. An infrared detector can receive the data carried in the infrared signal, and appropriate circuitry can place that data onto bus 902. Bus 902 transports the data to main memory 906, from which the processor 904 retrieves and executes the instructions. Instructions received by main memory 906 may optionally be stored on storage device 910 before or after execution by processor 904.

[0177] The computer system 900 also includes a communication interface 918 coupled to bus 902. The communication interface 918 provides bidirectional data communication coupling to a network link 920 connected to a local network 922. For example, the communication interface 918 may be an Integrated Services Digital Network (ISDN) card, cable modem, satellite modem, or modem for providing data communication connectivity to a corresponding type of telephone line. As another example, the communication interface 918 may be a Local Area Network (LAN) card for providing data communication connectivity to a compatible LAN. A wireless link may also be implemented. In any such implementation, the communication interface 918 transmits and receives electrical, electromagnetic, or optical signals carrying digital data streams representing various types of information.

[0178] Network link 920 typically provides data communication to other data devices through one or more networks. For example, network link 920 may provide connection to data facilities operated by a host computer 924 or an Internet service provider (ISP) 926 through a local network 922. ISP 926 provides data communication services through a global packet data network now commonly referred to as the “Internet” 928. Both the local network 922 and the Internet 928 use electrical, electromagnetic, or optical signals that carry digital data streams. Signals across various networks and signals on network link 920 and through communication interface 918 that carry digital data to and from computer system 900 are exemplary forms of transmission media.

[0179] The computer system 900 can send messages and receive data, including program code, through a network(s), network link 920, and communication interface 918. In the case of the internet, server 930 may send requested code for an application program through the internet 928, ISP 926, local network 922, and communication interface 918.

[0180] The received code may be executed by the processor 904 upon receipt, and / or stored in the memory device 910 or other non-volatile memory for later execution.

[0181] <12. Numbering Examples, Equivalents, Extensions, Substitutions, Others> The enumerated example embodiments (EEE) of the present invention have been described above in relation to image data exchange based on perceptual luminance nonlinearity across displays with different functions. Thus, embodiments of the present invention may relate to one or more of the examples listed in Table 2 below.

[0182] Table 2: Numbering Examples [EEE1] The stage of receiving image data to be encoded; A step of encoding received image data into reference-encoded image data based on a reference mapping between a set of reference digital code values ​​and a set of reference gray levels, wherein the luminance values ​​in the received image data are represented by the set of reference digital code values, the luminance difference between two reference gray levels in the received image data is represented by two adjacent reference digital code values ​​in the set of reference digital code values, and the luminance difference between two adjacent reference digital code values ​​is inversely proportional to the peak contrast sensitivity of human vision adapted to a certain light level; The step includes outputting the aforementioned reference-encoded image data, method. [EEE2] A method according to EEE1, further comprising the step of determining a reference grayscale display function (GSDF) based on a contrast sensitivity function (CSF), wherein the reference GSDF specifies the reference mapping between a set of reference digital code values ​​and a set of reference gray levels. [EEE3] A method according to EEE2, wherein the CSF model includes one or more model parameters, the one or more model parameters having an angular size that falls within a range including one or more of the following: between 25°×25° and 30°×30°, between 30°×30° and 35°×35°, between 35°×35° and 40°×40°, between 40°×40° and 45°×45°, or greater than 45°×45°. [EEE4] The method described in EEE1: The steps include: assigning an intermediate luminance value within the range of luminance values ​​supported by the set of reference gray levels to an intermediate digital code value in a code space accommodating the set of reference digital code values; A step of deriving a plurality of submappings by performing one or more summation or integral calculations, wherein each submapping maps the reference digital sign values ​​in the set of reference digital sign values ​​to the reference gray levels in the set of reference gray levels, method. [EEE5] A method according to EEE4, wherein the intermediate luminance value is selected from a range that includes one or more of the following: less than 50 nits, between 50 nits and 100 nits, between 100 nits and 500 nits, or greater than 500 nits. [EEE6] A method according to EEE1, wherein the set of reference gray levels covers a dynamic range having an upper limit of less than 500 nits, between 500 nits and 1000 nits, between 1000 nits and 5000 nits, between 5000 nits and 10000 nits, between 10000 nits and 15000 nits, or greater than 15000 nits. [EEE7] The method according to EEE1, wherein the peak contrast sensitivity is determined from one of several contrast sensitivity curves determined based on a contrast sensitivity function (CSF) model having model parameters including a luminance value variable, a spatial frequency variable, or one or more of the other variables. [EEE8] The method according to EEE7, wherein at least two peak contrast sensitivities determined based on at least two of the plurality of contrast sensitivity curves appear at two different spatial frequency values. [EEE9] The method according to EEE1, further comprising the step of converting one or more input images, which are to be represented, received, transmitted or stored, using the image data to be encoded from an input video signal, into one or more output images, which are to be represented, received, transmitted or stored, using the reference encoded image data included in an output video signal. [EEE10] The method according to EEE1, wherein the image data to be encoded includes image data encoded in one of the following: a high-resolution high dynamic range (HDR) image format, an RGB color space associated with the Academy of Motion Picture Arts and Sciences (AMPAS) Academy Color Encoding Standard (ACES) standard, the Digital Cinema Initiative P3 color space standard, a reference input medium metric / reference output medium metric (RIMM / ROMM) standard, an sRGB color space, or an RGB color space associated with the International Telecommunication Union (ITU) BT.709 Recommendation standard. [EEE11] The method according to EEE1, wherein the luminance difference between two reference gray levels represented by two adjacent reference digital code values ​​is less than the minimum discernible difference (JND) threshold at the particular light level. [EEE12] The method according to EEE1, wherein the specific light level is the luminance value between the two luminance values ​​(including both ends). [EEE13] The method according to EEE1, wherein the set of reference digital code values ​​has integer values ​​in a code space having a bit depth of 14 bits or more, less than 12 bits; between 12 bits and 14 bits; at least 14 bits; or 14 bits or more. [EEE14] The method according to EEE1, wherein the set of reference gray levels has a set of quantized luminance values. [EEE15] The method according to EEE1, wherein the reference GSDF is determined based on a functional model expressed at least in part using one or more functions. [EEE16] The method according to EEE15, wherein the function model has one or more model parameters, and the values ​​of the model parameters are optimized by minimizing the deviation between a predicted sign value and a target sign value. [EEE17] A step of determining a digital code mapping between a set of reference digital code values ​​and a set of device-specific digital code values, wherein the set of reference digital code values ​​are mapped to a set of reference gray levels in the reference mapping, and the set of device-specific digital code values ​​are mapped to a set of device-specific gray levels in the device-specific mapping; The step of receiving reference-encoded image data encoded with the aforementioned set of reference digital code values, wherein the luminance values ​​in the reference-encoded image data are based on the aforementioned set of reference digital code values, and the luminance difference between two reference gray levels represented by two adjacent reference digital code values ​​in the aforementioned set of reference digital code values ​​is inversely proportional to the peak contrast sensitivity of human vision adapted to a particular light level; The steps include transcoding the reference-encoded image data encoded with the set of reference digital code values ​​based on the digital code mapping into device-specific image data encoded with the set of device-specific digital control codes, wherein the luminance values ​​in the device-specific image data are based on the set of device-specific digital code values, method. [EEE18] The method described in EEE17: A step of determining a set of correspondences between a set of reference digital code values ​​and a set of device-specific digital code values, wherein the correspondences in the set of correspondences relate the reference digital code values ​​in the set of reference digital code values ​​to the device-specific digital code values; A step of comparing a first luminance difference in the reference digital code value with a second luminance difference in the device-specific digital code value; The process includes a step of storing algorithmic flags indicating whether dithering should be performed on the reference digital code value, whether contour removal should be performed, or whether no operation should be performed, based on a comparison of the first luminance difference and the second luminance difference. method. [EEE19] A step of determining a reference digital code value from the reference encoded image data for a certain pixel; The further step includes determining whether an algorithm flag is set for the reference digital code value. Method as described in EEE17. [EEE20] The method described in EEE19 further includes the step of performing a contour removal algorithm on a pixel in response to determining that an algorithm flag for contour removal has been set. [EEE21] The method described in EEE19 further includes the step of performing a dithering algorithm on a pixel in response to determining that an algorithm flag for dithering is set. [EEE22] The method according to EEE17, further comprising the step of rendering one or more images on a display based on the device-specific image data encoded with the set of device-specific digital control codes, wherein the display is one of a visible dynamic range (VDR) display, a standard dynamic range (SDR) display, a tablet computer display, or a handheld device display. [EEE23] The method according to EEE17, wherein a device-specific grayscale display function (GSDF) specifies the device-specific mapping between a set of device-specific digital code values ​​and a set of device-specific gray levels. [EEE24] The device-specific mapping is derived based on one or more display parameters and zero or more browsing condition parameters, according to the method described in EEE17. [EEE25] The method according to EEE17, wherein the gray level specific to the set of devices covers a dynamic range having an upper limit of less than 100 nits, 100 nits or more but less than 500 nits, between 500 nits and 1000 nits, between 1000 nits and 5000 nits, between 5000 nits and 10000 nits, or greater than 10000 nits. [EEE26] The method according to EEE17, further comprising the step of converting one or more input images, which are represented, received, transmitted or stored, using the reference encoded image data from an input video signal, into one or more output images, which are represented, received, transmitted or stored, using the device-specific image data included in an output video signal. [EEE27] The device-specific image data supports image rendering in one of the following formats: a high-resolution high dynamic range (HDR) image format, an RGB color space associated with the Academy of Motion Picture Arts and Sciences (AMPAS) Academy Color Encoding Standard (ACES) standard, the Digital Cinema Initiative P3 color space standard, the Reference Input Medium Metric / Reference Output Medium Metric (RIMM / ROMM) standard, the sRGB color space, or the RGB color space associated with the International Telecommunication Union (ITU) BT.709 Recommendation standard, as described in EEE17. [EEE28] The method according to EEE17, wherein the luminance difference between two reference gray levels represented by two adjacent reference digital code values ​​is less than the minimum known difference threshold at the particular light level. [EEE29] The method according to EEE17, wherein the specific light level is the luminance value between the two luminance values ​​(including both ends). [EEE30] The method according to EEE17, wherein the set of device-specific digital code values ​​have integer values ​​in a code space having a bit depth of 8 bits; greater than 8 bits but less than 12 bits; or 12 bits or more. [EEE31] The method according to EEE17, wherein the set of device-specific gray levels has a set of quantized luminance values. [EEE32] The method according to EEE17, wherein at least one of the reference mapping and the device-specific mapping is determined at least in part based on a function model expressed using one or more functions. [EEE33] The method according to EEE32, wherein the function model has one or more model parameters, and the values ​​of the model parameters are optimized by minimizing the deviation between a predicted sign value and a target sign value. [EEE34] An encoder that performs the method described in any one of EEE1 to EEE16. [EEE35] A decoder that performs the method described in any one of EEE17 to 33. [EEE36] A system that implements the method described in any one of EEE1 to 33. [EEE37] A system having an encoder and a decoder, The aforementioned encoder is: The stage of receiving image data to be encoded; A step of encoding received image data into reference-encoded image data based on a reference mapping between a set of reference digital code values ​​and a set of reference gray levels, wherein the luminance values ​​in the image data to be encoded are represented by the set of reference digital code values, and the luminance difference between two reference gray levels represented by two adjacent reference digital code values ​​in the set of reference digital code values ​​is inversely proportional to the peak contrast sensitivity of human vision adapted to a particular light level; The system is configured to perform the step of outputting the aforementioned reference-encoded image data. The aforementioned decoder is: A step of determining a digital code mapping between a set of reference digital code values ​​and a set of device-specific digital code values, wherein the set of device-specific digital code values ​​are mapped to a set of device-specific gray levels in the device-specific mapping; The step of receiving the aforementioned reference-encoded image data; The system is configured to perform the following steps: transcoding the reference-encoded image data encoded with the set of reference digital code values ​​based on the digital code mapping into device-specific image data encoded with the set of device-specific digital control codes, wherein the luminance values ​​in the device-specific image data are based on the set of device-specific digital code values. system. [EEE38] It is an image decoder: A mapping decision-maker that determines a digital code mapping between a set of reference digital code values ​​and a set of device-specific digital code values, wherein the set of reference digital code values ​​are mapped to a set of reference gray levels in the reference mapping, and the set of device-specific digital code values ​​are mapped to a set of device-specific gray levels in the device-specific mapping; A receiver for receiving reference-encoded image data encoded with the aforementioned set of reference digital code values, wherein the luminance values ​​in the reference-encoded image data are based on the aforementioned set of reference digital code values, and the luminance difference between two reference gray levels represented by two adjacent reference digital code values ​​in the aforementioned set of reference digital code values ​​is inversely proportional to the peak contrast sensitivity of human vision adapted to a certain light level; A transcoder that transcodes the reference-encoded image data encoded with the set of reference digital code values ​​based on the digital code mapping into device-specific image data encoded with the set of device-specific digital control codes, wherein the luminance values ​​in the device-specific image data are based on the set of device-specific digital code values. decoder. [EEE39] Decoder as described in EEE38: Determining a set of correspondence relationships between the set of reference digital code values and the set of device-specific digital code values, wherein the correspondence relationships in the set of correspondence relationships associate the reference digital code values in the set of reference digital code values with the device-specific digital code values; Comparing a first luminance difference in the reference digital code value with a second luminance difference in the device-specific digital code value; Based on the comparison of the first luminance difference and the second luminance difference, storing an algorithm flag, wherein the algorithm flag functions to flag whether dithering should be performed, contour removal should be performed, or no operation should be performed for the reference digital code value; Decoder. 〔EEE40〕 Determining a reference digital code value from the reference-encoded image data for a pixel; Further configured to execute a step of determining whether an algorithm flag is set for the reference digital code value; The decoder according to EEE38. 〔EEE41〕 The decoder according to EEE40, further configured to execute a contour removal function for the pixel in response to determining that an algorithm flag for contour removal is set. 〔EEE42〕 The decoder according to EEE40, further configured to execute a dithering process for the pixel in response to determining that an algorithm flag for dithering is set. 〔EEE43〕 The decoder described in EEE38 is further configured to perform the step of rendering one or more images on a display based on the device-specific image data encoded with the set of device-specific digital control codes, wherein the display is one of a visible dynamic range (VDR) display, a standard dynamic range (SDR) display, a tablet computer display, or a handheld device display. [EEE44] The decoder according to EEE38, wherein a device-specific grayscale display function (GSDF) specifies the device-specific mapping between a set of device-specific digital code values ​​and a set of device-specific gray levels. [EEE45] The decoder according to EEE38, wherein the device-specific mapping is derived based on one or more display parameters and zero or more browsing condition parameters. [EEE46] The decoder according to EEE38, wherein the set of device-specific gray levels spans a dynamic range with an upper limit having values ​​less than 100 nits, 100 nits or more but less than 500 nits, between 500 nits and 1000 nits, between 1000 nits and 5000 nits, between 5000 nits and 10000 nits, or greater than 10000 nits. [EEE47] The decoder according to EEE38, further comprising a converter that converts one or more input images, which are represented, received, transmitted or stored, using the reference encoded image data from an input video signal, into one or more output images, which are represented, received, transmitted or stored, using the device-specific image data included in an output video signal. [EEE48] The device-specific image data is a decoder described in EEE38, which supports image rendering in one of the following formats: high-resolution high dynamic range (HDR) image format, RGB color space associated with the Academy of Motion Picture Arts and Sciences (AMPAS) Academy Color Encoding Standard (ACES) standard, Digital Cinema Initiative P3 color space standard, reference input medium metric / reference output medium metric (RIMM / ROMM) standard, sRGB color space, or RGB color space associated with the International Telecommunication Union (ITU) BT.709 recommendation standard. [EEE49] The decoder according to EEE38, wherein the luminance difference between two reference gray levels, represented by two adjacent reference digital code values, is less than the minimum discernible difference (JND) threshold at the given light level. [EEE50] The decoder according to EEE38, wherein the aforementioned specific light level is a luminance value that lies between (including both ends of) the two aforementioned luminance values. [EEE51] The decoder according to EEE38, wherein the set of device-specific digital code values ​​have integer values ​​in a code space having a bit depth of 8 bits; greater than 8 bits but less than 12 bits; or 12 bits or more. [EEE52] The decoder according to EEE31, wherein the set of device-specific gray levels has a set of quantized luminance values. [EEE53] The decoder according to EEE38, wherein at least one of the reference mapping and the device-specific mapping is determined at least in part based on a function model expressed using one or more functions. [EEE54] The decoder according to EEE53, wherein the function model has one or more model parameters, the values ​​of the model parameters are optimized by minimizing the deviation between a predicted sign value and a target sign value. [EEE55] A non-temporary computer-readable storage medium in which instructions are encoded and stored, wherein, when executed by a computer or its processor, the instructions cause the computer or its processor to execute, run or control, or control, or program in such a way as to decode an image process, the image decoding process being: A step of determining a digital code mapping between a set of reference digital code values ​​and a set of device-specific digital code values, wherein the set of reference digital code values ​​are mapped to a set of reference gray levels in the reference mapping, and the set of device-specific digital code values ​​are mapped to a set of device-specific gray levels in the device-specific mapping; The step of receiving reference-encoded image data encoded with the aforementioned set of reference digital code values, wherein the luminance values ​​in the reference-encoded image data are based on the aforementioned set of reference digital code values, and the luminance difference between two reference gray levels represented by two adjacent reference digital code values ​​in the aforementioned set of reference digital code values ​​is inversely proportional to the peak contrast sensitivity of human vision adapted to a particular light level; A step of transcoding the reference-encoded image data encoded with the set of reference digital code values ​​based on the digital code mapping into device-specific image data encoded with the set of device-specific digital control codes, wherein the luminance is... [EEE56] Means for determining a digital code mapping between a set of reference digital code values ​​and a set of device-specific digital code values, wherein the set of reference digital code values ​​are mapped to a set of reference gray levels in the reference mapping, and the set of device-specific digital code values ​​are mapped to a set of device-specific gray levels in the device-specific mapping; A means for receiving reference-encoded image data encoded with the aforementioned set of reference digital code values, wherein the luminance values ​​in the reference-encoded image data are based on the aforementioned set of reference digital code values, and the luminance difference between two reference gray levels represented by two adjacent reference digital code values ​​in the aforementioned set of reference digital code values ​​is inversely proportional to the peak contrast sensitivity of human vision adapted to a particular light level; A means for transcoding the reference-encoded image data encoded with the set of reference digital code values ​​based on the digital code mapping into device-specific image data encoded with the set of device-specific digital control codes, wherein the luminance values ​​in the device-specific image data are based on the set of device-specific digital code values. Digital decoding system. [EEE57] A step of receiving reference-encoded image data encoded with a reference code value, wherein the reference code value represents a set of reference gray levels, the first pair of adjacent gray levels in the set of gray levels relates to a first peak contrast sensitivity of human vision adapted to a first light level, and the second pair of adjacent gray levels in the set of gray levels relates to a second peak contrast sensitivity of human vision adapted to a second different light level; A step of accessing a code mapping between a reference code value and a device-specific code value, wherein the device-specific code value represents a set of device-specific gray levels; The step of transcoding the reference-encoded image data to device-specific image data encoded with the device-specific control code, based on the code mapping, method. [EEE58] A method as described in EEE57, wherein the set of reference gray levels covers a dynamic range having an upper limit of less than 500 nits, between 500 nits and 1000 nits, between 1000 nits and 5000 nits, between 5000 nits and 10000 nits, between 10000 nits and 15000 nits, or greater than 15000 nits. [EEE59] The aforementioned set of reference gray levels is constructed based on a human visual model that supports a field of view greater than 40 degrees, as described in EEE57. [EEE60] The aforementioned set of reference gray levels relates to a variable spatial frequency lower than the cutoff spatial frequency, as described in EEE57. [EEE61] The method according to EEE57, wherein the code mapping is configured to distribute perceptually recognizable errors evenly within the dynamic range covered by the device-specific gray level. [EEE62] The method according to EEE57, wherein the difference in first luminance values ​​of adjacent gray levels in the set of gray levels is inversely related to the first peak contrast sensitivity by some multiplication constant, and the difference in first luminance values ​​of adjacent gray levels in the second pair is inversely related to the second peak contrast sensitivity by the same multiplication constant. [EEE63] The method described in EEE57, wherein the reference sign value in the aforementioned reference sign value and the reference gray level represented by the reference sign value have different numerical values. [EEE64] A method described in EEE57, comprising the step of transcoding the reference-encoded image data to device-specific image data encoded with device-specific control codes based on the code mapping: The step of determining the first luminance value difference between two adjacent reference sign values ​​in a given reference sign value; The steps include determining the second luminance value difference between two adjacent device-specific code values ​​in the device-specific code values ​​corresponding to the aforementioned reference code value; Based on a comparison of the first luminance value difference and the second luminance value difference, applying one of a dithering algorithm or a contour removal algorithm to at least one pixel in the device-specific image data, Method. 〔EEE65〕 An image processing apparatus comprising: A data receiver configured to receive reference-encoded image data having reference code values, wherein the reference-encoded image data is encoded by an external encoding system, the reference code values represent reference gray levels, and the reference gray levels are selected using a reference gray scale display function based on the perceptual non-linearity of human vision adapted to various spatial frequencies at various light levels, a data receiver; A data converter configured to access a code mapping between the reference code values and device-specific code values of the image processing apparatus, wherein the device-specific code values are configured to generate device-specific gray levels configured for the image processing apparatus, and the data converter is configured to transcoding the reference-encoded image data into device-specific image data encoded with the device-specific code values based on the code mapping, a data converter; The image processing apparatus is at least one of: a game machine, a television, a laptop computer, a desktop computer, a netbook computer, a computer workstation, a cellular radio phone, an e-book reader, a point-of-sale terminal, and a computer kiosk. Image processing apparatus.

[0183] Table 3 below describes the calculation of a perceptual curve EOTF for converting digital video code values to absolute linear luminance levels in terms of display. It also includes the inverse OETF for converting absolute linear luminance to digital code values.

[0184] Table 3 Exemplary specifications for the perceptual curve EOTF Definition of illustrative formulas: D = Digital sign value of the perceptual curve, SDI legal unsigned integer, 10 or 12 bits b = number of bits per component in the digital signal representation, 10 or 12 V = Signal value of the normalized perceptual curve, 0 ≤ V ≤ 1 Y = normalized luminance value, 0 ≤ Y ≤ 1 L = absolute luminance value, 0 ≤ L ≤ 10,000 cd / m 2 Exemplary EOTF decoding formula

number

number

number

[0185] Table 4 below shows exemplary values ​​for 10 bits.

[0186] Table 4: An example table of values ​​for 10 bits.

[0187] [Table 2]

[0188] [Table 3]

[0189] [Table 4]

[0190] [Table 5]

[0191] [Table 6]

[0192] [Table 7]

[0193] [Table 8]

[0194] [Table 9]

[0195] [Table 10]

[0196] [Table 11]

[0197] [Table 12] [In the table, "Reserved" indicates that the item is reserved.] The above specification has described embodiments of the present invention, referring to numerous specific details that may vary depending on the implementation. Thus, the sole and exclusive indicator of what constitutes the present invention, and what the applicant intends to constitute the present invention, is the specific form in which such claims are patented, including any subsequent amendments. If any definitions of terms included in such claims are explicitly stated in this document, those definitions govern the meaning of such terms as used in the claims. Therefore, no limitations, elements, attributes, features, advantages, or characteristics not explicitly stated in the claims should in any way limit the scope of such claims. Accordingly, the specification and drawings should be considered illustrative and not restrictive.

[0198] Several aspects are described below. [Aspect 1] An image processing device: A data receiver configured to receive reference-encoded image data having a reference code value, wherein the reference-encoded image data is encoded by an external encoding system, the reference code value represents a reference gray level, and the reference gray level is selected using a reference grayscale display function based on the perceptual nonlinearity of human vision adapted to various spatial frequencies at various light levels; A data converter configured to access a code mapping between a reference code value and an instrument-specific code value of the image processing device, wherein the instrument-specific code value is configured to produce an instrument-specific gray level configured for the image processing device, and the data converter is configured to transcode the reference encoded image data to instrument-specific image data encoded with the instrument-specific code value, based on the code mapping. Image processing device. [Aspect 2] An image processing apparatus according to embodiment 1, which is a standard dynamic range display device. [Aspect 3] The image processing apparatus according to embodiment 1, configured to support either an intermediate horizontal angle size between 40 degrees and 25 degrees, or a narrow horizontal angle size of 25 degrees or less. [Aspect 4] The image processing apparatus according to embodiment 1, configured to support either an intermediate viewing distance of 1.5 meters to 0.5 meters or a short viewing distance of 0.5 meters or less. [Aspect 5] A step of receiving reference-encoded image data encoded with a reference code value, wherein the reference code value represents a set of reference gray levels; A step of accessing a code mapping between a reference code value and a device-specific code value, wherein the device-specific code value represents a set of device-specific gray levels; The process includes the step of transcoding the reference-encoded image data to device-specific image data encoded with the device-specific control code, based on the code mapping. The first quantization step between the successive reference gray levels of the aforementioned set is related to the contrast sensitivity of human vision adapted to spatial frequencies at the first light level. method. [Aspect 6] The method according to embodiment 5, wherein the first quantization step size is related to the peak contrast sensitivity of human vision adapted to spatial frequency with respect to the first light level. [Aspect 7] The method according to embodiment 5, wherein the first quantization step size is smaller than the minimum known difference threshold at the first optical level. [Aspect 8] The method according to embodiment 5, wherein the second quantization step is related to the contrast sensitivity of human vision adapted to spatial frequencies at the second light level, and the first and second quantization step sizes are different. [Aspect 9] The method according to embodiment 8, wherein the second quantization step size is related to the peak contrast sensitivity of human vision adapted to spatial frequency with respect to the first light level. [Aspect 10] The method according to embodiment 8, wherein the first light level is smaller than the second light level, and the first notch size is larger than the second notch size. [Aspect 11] The method according to embodiment 5, wherein the set of reference gray levels covers a dynamic range having an upper limit of values ​​between 1,000 nits and 15,000 nits.

Claims

1. A computer program having instructions, wherein, when the instructions are executed by a computing device or system, the computing device or system: The stage of receiving encoded image data; The step of accessing the EOTF decode function for decoding the encoded image data. This will cause it to execute, The EOTF decoding function maps the digital code value D in the encoded image data to a normalized luminance value, and at least in part [Number 13] Based on the function model, Y is a normalized luminance value, where 0 ≤ Y ≤ 1. V is the normalized value of the corresponding digital code value D of the encoded image data, where 0 ≤ V ≤ 1. n, m, c 1 , c 2 , c 3 This is a predetermined value, [Number 14] That is A computer program characterized by the following.

2. The computer program according to claim 1, wherein the digital code value D is a 10-bit code value.

Images