Device and method of improving perceptual luminance nonlinearity-based image data exchange across different display capabilities

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-REFERENCE TO RELATED APPLICATIONS This application claims priority to U.S. Provisional Patent Application No. 61 / 567,579, filed December 6, 2011, U.S. Provisional Patent Application No. 61 / 674,503, filed July 23, 2012, and U.S. Provisional Patent Application No. 61 / 703,449, filed September 20, 2012, the contents of all of which are hereby incorporated by reference in their entirety for all purposes.

[0002] The technology of the present invention The present invention relates generally to image data, and more particularly, to perceptually non-linear image data exchange between different display capabilities. [Background technology]

[0003] Technological advances have enabled modern display designs to render image and video content with significant improvements in various quality characteristics relative to the same content rendered on earlier displays. For example, some newer displays can render content with a higher dynamic range (DR) than the standard dynamic range (SDR) of ordinary 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 whose individual portions can be modulated independently of the modulation of the liquid crystal alignment state of the active LCD elements. This dual modulation approach can be extended (e.g., to N modulation layers, where N is an integer greater than 2) by adding controllable intervening layers (e.g., multiple individually controllable LCD layers) in the electro-optical configuration of the display.

[0005] In contrast, some existing displays have a significantly narrower dynamic range (DR) than high dynamic range (HDR). Mobile devices, computer pads, game consoles, televisions (TVs), and computer monitors that use typical cathode ray tube (CRT), liquid crystal display (LCD) with constant fluorescent white backlight, or plasma screen technology can be limited by nearly three orders of magnitude in their DR rendering capabilities. Thus, such existing displays are representative of standard dynamic range (SDR), sometimes referred to as "low dynamic range" (LDR) in the context of 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) Summary of the Invention [Problem to be solved by the invention]

[0007] Images captured by HDR cameras may have a scene-referred HDR that is significantly larger than the dynamic range of most, if not all, display devices. Scene-referred HDR images contain a large amount of data and may be converted into post-production formats (e.g., HDMI® video signals with 8-bit RGB, YCbCr, or deep color options; 1.5 Gbps SDI video signals with 10-bit 4:2:2 sampling rates; 3 Gbps SDI with 12-bit 4:4:4 or 10-bit 4:2:2 sampling rates; and other video or image formats) for easier transmission and storage. Post-production images have a much smaller dynamic range than scene-referred HDR images. Furthermore, when images are delivered to an end-user's display device for rendering, device-specific and / or manufacturer-specific image transformations occur along the way, causing significant visually perceptible errors 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 be pursued, but are not necessarily approaches that have been previously conceived or pursued. Thus, unless otherwise noted, it should not be assumed that any approach described in this section qualifies as prior art merely by virtue of its inclusion in this section. Likewise, unless otherwise noted, it should not be assumed that problems identified with one or more approaches have been recognized in any prior art by virtue of this section. [Brief explanation of the drawings]

[0009] The present invention is illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings in which like reference symbols refer to similar elements and in which: [Figure 1]FIG. 10 illustrates an exemplary family of curves for contrast sensitivity functions across multiple light adaptation levels, in accordance with an exemplary embodiment of the present invention. [Figure 2] FIG. 2 illustrates an exemplary integral path according to an exemplary embodiment of the present invention. [Figure 3] FIG. 2 illustrates an exemplary grayscale display function in accordance with an exemplary embodiment of the present invention. [Figure 4] 1 is a curve illustrating the Weber fraction in accordance with an example embodiment of the present invention. [Figure 5] 1 illustrates an exemplary framework for the exchange of image data by devices of various GSDFs in accordance with an exemplary embodiment of the present invention. [Figure 6] FIG. 2 illustrates an exemplary conversion unit according to an exemplary embodiment of the present invention. [Figure 7] FIG. 1 illustrates an exemplary SDR display in accordance with an exemplary embodiment of the present invention. [Figure 8] 1A and 1B illustrate an exemplary process flow according to an exemplary embodiment of the present invention. [Figure 9] FIG. 1 illustrates an exemplary hardware platform upon which a computer or computing device described herein may be implemented, in accordance with an exemplary embodiment of the present invention. [Figure 10A] FIG. 1 illustrates the maximum code error in JNDs for multiple code spaces, each having one of one or more different bit lengths, according to some example embodiments. [Figure 10B] FIG. 10 illustrates a distribution of sign errors according to some example embodiments. [Figure 10C] FIG. 10 illustrates a distribution of sign errors according to some example embodiments. [Figure 10D] FIG. 10 illustrates a distribution of sign errors according to some example embodiments. [Figure 10E] FIG. 10 illustrates a distribution of sign errors according to some example embodiments. [Figure 11] FIG. 10 illustrates values ​​of parameters in a function model according to an example embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0010] Exemplary embodiments relating to image data exchange based on perceptual luminance nonlinearity between displays of different capabilities are described herein. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, it will be apparent that the present invention may be practiced without such specific details. In other instances, well-known structures and devices are not described in exhaustive detail in order to avoid unnecessarily obscuring, burying, or obscuring the present invention.

[0011] Exemplary embodiments are described herein according to the following outline.

[0012] 1. Overview 2. Contrast Sensitivity Function (CSF) Model 3. Perceptual nonlinearity 4. Digital Code Values ​​and Gray Levels 5. Model Parameters 6. Variable spatial frequency 7. Function Model 8. Image data exchange based on the reference GSDF 9. Conversion of Reference-encoded Image Data 10. Exemplary Process Flow 11. Implementation Mechanism - Hardware Overview 12. Numbering Examples, Equivalents, Extensions, Alternatives, and Other

[0013] 1. Overview This overview provides a basic description of some aspects of certain embodiments of the present invention. It should be noted that this overview is not a comprehensive or exhaustive summary of aspects of the exemplary embodiments. Moreover, this overview is not intended to identify any particularly significant aspects or elements of the exemplary embodiments, nor is it intended to delineate any scope of the present invention in general, or of the exemplary embodiments in particular. This overview merely presents some concepts related to the exemplary embodiments in a condensed and simplified form, and should merely be understood as a conceptual prelude to the more detailed description of the exemplary embodiments that follows.

[0014] Human vision may not perceive a difference between two luminance values ​​if the two luminance values ​​are not sufficiently different from each other. Human vision only perceives a difference if the luminance values ​​differ by at least a just noticeable difference (JND). Due to nonlinearities in human visual perception, the magnitude of an individual JND is not uniformly sized or scaled across a range of light levels, but rather varies with different individual light levels. Furthermore, due to nonlinearities in perception, the magnitude of an individual JND is not uniformly sized or scaled across a range of spatial frequencies at a particular light level, but rather varies with different spatial frequencies below a certain cutoff spatial frequency.

[0015] Encoded image data with luminance quantization steps of equal size or linearly scaled size does not match the perceptual non-uniformity of human vision. Encoded image data with luminance quantization steps at a fixed spatial frequency does not match the perceptual non-uniformity of human vision. Under these techniques, when code words are assigned to represent quantized luminance values, too many code words may be distributed in certain regions (e.g., bright regions) in the range of light levels, while too few code words may be distributed in different regions (e.g., dark regions) of the range of light levels.

[0016] In overdistributed regions, many of the codewords may not make a perceptual difference and are therefore effectively wasted. In underdistributed regions, two adjacent codewords may make a perceptual difference much greater than the JND, potentially resulting in visual artifacts such as contour distortion (also known as banding).

[0017] Under the techniques described herein, a wide range of light levels (e.g., 0 to 12,000 cd / m) can be achieved. 2 A contrast sensitivity function (CSF) model may be used to determine the JND over a range of luminance values. 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 is consistent with the behavior of human vision, which adapts to increased levels of visual perceptibility when viewing backgrounds of close but different luminance values. This is sometimes referred to in the field of video and image display as the crispening effect and / or Whittle's crispening effect, and will be referred to as such herein. As used herein, "light adaptation level" may be used to refer to the light level at which a (e.g., peak) JND is selected / determined, assuming human vision has adapted to that light level. The peak JNDs described herein vary across spatial frequencies at different light adaptation levels.

[0018] As used herein, the term "spatial frequency" can refer to the rate of spatial modulation / variation in an image (where, rather than calculating the rate with respect to time, the rate is calculated in relation to or with respect to spatial distance). In contrast to typical approaches where spatial frequency may be fixed at a particular value, the spatial frequencies described herein can vary, for example, within or over a range. In some embodiments, the peak JND can be limited to a particular spatial frequency range (e.g., 0.1 to 5.0, 0.01 to 8.0 cycles / degree, or a smaller or larger range).

[0019] Based on the CSF model, a reference gray scale display function (GSDF) may be generated. In some embodiments, a very wide field of view is assumed for the CSF model to generate a reference GSDF that better supports the entertainment display field. The GSDF refers to a set of reference digital code values ​​(or reference code words), a set of reference gray levels (or reference luminance values), and a mapping between both sets. In an exemplary embodiment, each reference digital code value corresponds to a quantum of human perception represented by a JND (e.g., a peak JND at a light adaptation level). In an exemplary embodiment, an equal number of reference digital code values ​​may correspond to quanta of human perception.

[0020] The GSDF is obtained by accumulating the JND from an initial value. In one exemplary embodiment, the median codeword value (e.g., 2048 for a 12-bit code space) is provided 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 / m 2 ) Other reference gray levels for other values ​​of the reference digital code are obtained by positively accumulating (adding) the JND when the reference digital code is incremented by one, and by negatively accumulating (subtracting) the JND when the reference digital code is decremented by one. In an exemplary embodiment, a quantity such as a contrast threshold may be used in the calculation of the reference values ​​in the GSDF instead of the JND. These quantities actually used in the calculation of the GSDF may be defined as unitless ratios and may differ from the corresponding JND by a known or determinable multiplier, division factor, and / or offset.

[0021] The code space may be selected to include 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 larger or smaller code spaces.

[0022] Although a large code space (>15 bits) may be used to accommodate all reference digital code values, 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.

[0023] The reference GSDF may be used to encode image data captured or generated, for example, by an HDR camera, studio / system, or other system with a scene-based HDR that is significantly greater than the dynamic range of most, if not all, display devices. The encoded image data may be transmitted over a wide variety of delivery or transmission methods (e.g., 8-bit RGB, YCbCr, etc.). or HDMI® video signals with deep color options; 1.5Gbps SDI video signals with 10-bit 4:2:2 sampling rates; 3Gbps SDI with 12-bit 4:4:4 or 10-bit 4:2:2 sampling rates; and other video or image formats) may be provided to downstream devices.

[0024] In some embodiments, adjacent reference digital code values ​​in the reference GSDF correspond to gray levels within a JND, so that details distinguishable by human vision can be fully or substantially preserved in image data encoded based on the reference GSDF, and a display that fully supports the reference GSDF can potentially render images without banding or contour distortion artifacts.

[0025] Image data encoded based on a reference GSDF (or reference encoded image data) may be used to support a wide variety of less capable displays that may not fully support all of the reference luminance values ​​in the reference GSDF. Because the reference encoded image data has all perceptual detail within the supported luminance range (which may be designed to be a superset of what the display supports), the reference digital code values ​​may be optimally and efficiently transcoded to display-specific digital code values ​​in a manner 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 conjunction with or as part of the transcoding from the reference digital code values ​​to display-specific digital code values ​​to further improve image or video quality.

[0026] The techniques described herein are color-space independent and may be used in RGB, YCbCr, or different color spaces. Furthermore, techniques for deriving reference values ​​(e.g., reference digital code values ​​and reference gray levels) using JNDs that vary with spatial frequency may be applied to different channels (e.g., one of the red, green, and blue channels) other than the luminance channel in different color spaces (e.g., RGB) that may or may not include a luminance channel. For example, a JND applicable to the blue color channel may be used to derive a reference blue value instead of a reference gray level. Thus, in some embodiments, grayscale may be used in place 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.

[0027] In some embodiments, the mechanisms described herein are part of a media processing system, including but not limited to a handheld device, a gaming console, a television, a laptop computer, a netbook computer, a cellular wireless telephone, an e-reader, a point-of-sale terminal, a desktop computer, a computer workstation, a computer kiosk, or various other types of terminals and media processing units.

[0028] Various modifications to the preferred embodiment and general principles and features described herein will be apparent to those skilled in the art, and thus the present disclosure is not intended to be limited to the embodiment shown but is to be accorded the widest scope consistent with the principles and features described herein.

[0029] 2. Contrast Sensitivity Function (CSF) Model The sensitivity of human vision to spatial structures in a rendered image can best be described by a contrast sensitivity function (CSF), which expresses contrast sensitivity as a function of spatial perimeter. Contrast sensitivity S may be thought of as the gain in neural signal processing in human vision, while contrast threshold C T may be determined from the inverse of the contrast sensitivity, for example: Contrast sensitivity = S = 1 / C T (1) As used herein, "contrast threshold" may refer to or relate to the minimum (relative) contrast (e.g., noticeable difference) required for the human eye to perceive a difference in contrast. In some embodiments, the contrast threshold may be plotted as a function of the noticeable difference divided by the light adaptation level over a range of luminance values.

[0030] In some embodiments, the contrast threshold may be measured directly in an experiment without using any CSF model. However, in some other embodiments, the contrast threshold may be determined based on a CSF model. A CSF model may be constructed using several model parameters and used to derive a GSDF. The quantization step of the GSDF at gray levels depends on and varies with the light level and spatial frequency, as characterized by luminance values. Some exemplary embodiments may be implemented based on one or more of various CSF models, such as those described in Non-Patent Document 1 (hereinafter referred to as Barten's model or Barten's CSF model) or Non-Patent Document 2 (hereinafter referred to as Daly's model). In the context of exemplary embodiments of the present invention, the contrast threshold used to generate a reference grayscale display function (GSDF) may be derived experimentally, theoretically, using a CSF model, or a combination thereof.

[0031] In this paper, a GSDF is a set of multiple gray levels (L1, L2, L3, ..., L4) of multiple digital code values ​​(e.g., 1, 2, 3, ..., N). N ), where the digital code values ​​represent index values ​​of the contrast thresholds and the gray levels correspond to the contrast thresholds, as shown in Table 1.

[0032] [Table 1] In one embodiment, the gray level (e.g., L) corresponding to the digital code value (e.g., i) is i ) and adjacent gray levels (e.g., L i+1 ) may be calculated in relation to the contrast (e.g., C(i)) as follows:

[0033] 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) where C(i) is L i and L i+1 represents the contrast for the luminance range bounded by L. mean (i,i+1) are two adjacent gray levels L i and L i+1 The contrast C(i) is arithmetically related to the Weber fraction by a factor of 2, where ΔL is the arithmetic mean of (L i+1 -L i ), and L is L i , L i+1 Either one of the two or L i and L i+1 Represents an intermediate value.

[0034] In some embodiments, the GSDF generator calculates the contrast C(i) as a function of the inclusive L i and L i+1 The contrast threshold (say, C) at a luminance level L between T (i)) may be set to a value equal to or otherwise proportional to it.

[0035] C(i)=kC T (i) (3) where k represents a multiplication constant. In the context of embodiments of the present invention, other descriptive statistics / definitions (e.g., geometric mean, median, mode, variance, or standard deviation) and / or scaling (×2, ×3, divide by a scaling factor, multiply by a scaling factor, etc.) and / or offset (+1, +2, −1, −2, subtract an offset, add an offset, etc.) and / or weighting (e.g., assigning the same or different weight factors to two adjacent gray levels) may be used to relate the contrast threshold to the contrast for the purposes of calculating the gray levels in the GSDF.

[0036] As calculated by equations (1), (2), and (3), the contrast or contrast threshold is a relative value and therefore may be a dimensionless quantity (so, for example, S may also be a dimensionless quantity).

[0037] CSF models can be constructed from basic contrast threshold measurements or CSF-based calculations that describe the CSF model. Unfortunately, human vision is complex, adaptive, and nonlinear, so there is no single CSF curve that describes human vision. Instead, a family of CSF curves can be generated based on the CSF model. For the same CSF model, different values ​​of the model parameters will result in different plots for the family of CSF curves.

[0038] 3. Nonlinearity of Perception FIG. 1 shows an exemplary family of CSF curves spanning multiple light adaptation levels. For illustrative purposes only, the top CSF curve depicted in FIG. 1 is for 1000 candelas per square meter (cd / m 2 The first curve is for light adaptation levels at luminance values ​​of 1000 (or "nits"), while the other lower curves are for light adaptation levels at luminance values ​​decreasing by a factor of 10. A notable feature of these CSF curves is that as luminance increases (light adaptation levels increase), overall contrast sensitivity, including maximum (or peak) contrast sensitivity, increases. The peak spatial frequency at which contrast sensitivity peaks on the CSF curve in Figure 1 shifts to higher spatial frequencies. Similarly, the maximum perceptible spatial frequency (cutoff frequency) on the CSF curve, which is the point at which the CSF curve intercepts the horizontal axis (spatial frequency axis), also increases.

[0039] In one exemplary embodiment, the CSF function that gives the family of CSF curves shown in FIG. 1 may be derived using Barten's CSF model, which takes into account several key effects related to human perception. An exemplary CSF S(u) (or corresponding contrast threshold m) under Barten's CSF model is t The reciprocal of ( ) may be calculated as shown in equation (4) below:

[0040]

number

[0041] Optical modulation transfer function M opt may be given as follows:

[0042]

number

[0043] The Barten CSF model discussed above can be used to describe the nonlinearity of perception for luminance. Other CSF models may be used to describe the nonlinearity of perception. For example, the Barten CSF model does not take into account the effect of accommodation, which causes a decrease in the cutoff spatial frequency in the high spatial frequency region of the CSF. This decrease in the effect of accommodation may be expressed as a function of decreasing viewing distance.

[0044] For example, for viewing distances greater than 1.5 meters, the maximum cutoff spatial frequency described by Barten's CSF model may be reached, without affecting the validity of Barten's model as an appropriate model for describing perceptual nonlinearities. However, for distances shorter than 1.5 meters, the effect of accommodation begins to become significant, reducing the accuracy of Barten's model.

[0045] Thus, for tablet displays with closer viewing distances such as 0.5 meters, or smartphones that can have viewing distances as close as 0.125 meters, Barten's CSF model may not be optimally calibrated.

[0046] In some embodiments, a Daily CSF model that takes into account the effect of accommodation may be used. In one particular embodiment, the Daily CSF model is based in part on Berten's CSF S(u) in equation (4) above, e.g., on the optical modulation transfer function M in equation (5): opt It may be constructed by modifying

[0047] 4. Digital Code Values ​​and Gray Levels A GSDF such as that shown in Table 1 maps perceptual nonlinearities using digital code values ​​to represent gray levels associated with 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 nonlinearities of human vision.

[0048] 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 a manner to achieve a minimum number of gray levels (e.g., less than 4096 total digital code values) without causing visibility of gray level step transitions (e.g., visible as false contours or bands in the image; or color shifts in dark areas of the image).

[0049] In some other embodiments, a limited number of digital code values ​​may be used to represent a wide dynamic range of gray levels. For example, when the maximum number of grayscale levels in a GSDF is not large enough relative to 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 a manner that achieves a minimum number of gray levels (e.g., fewer than 256 total digital code values) 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 throughout the relatively small number of gray level hierarchies in the GSDF. As used herein, the terms "grayscale level" or "gray level" may be used interchangeably and may refer to the represented luminance value (the quantized luminance value represented in the GSDF).

[0050] Gray levels in the GSDF may be derived by integrating or summing contrast thresholds across light adaptation levels (at different luminance values). In some embodiments, the quantization step between gray levels may be chosen so that the quantization step between any two adjacent gray levels falls within the JND. The contrast threshold at a particular light adaptation level (or luminance value) may not be greater than the just noticeable difference (JND) at that particular adaptation level. Gray levels may be derived by integrating or summing fractions of the contrast thresholds (or JNDs). In some embodiments, the number of digital code values ​​is more than sufficient to represent all JNDs in the represented dynamic range of luminance.

[0051] The contrast thresholds, or conversely, contrast sensitivities, used to calculate grayscale levels may be selected from the CSF curve at different spatial frequencies other than a fixed spatial frequency for a particular light adaptation level (or luminance value). In some embodiments, each of the contrast thresholds is selected from the CSF curve at a spatial frequency corresponding to a peak contrast sensitivity (e.g., due to the Whittle sharpening effect) for the light adaptation level. Furthermore, contrast thresholds may be selected from the CSF curve at various spatial frequencies for various light adaptation levels.

[0052] An exemplary formula for calculating / accumulating gray levels in a GSDF is:

[0053]

number

[0054] In some embodiments, the accumulation of JNDs to derive gray levels in the GSDF is performed by summation, for example, as shown in Equation (6). In other embodiments, integration may be used instead of discrete summation. The integration may be along an integration path determined from the CSF (e.g., Equation (4)). For example, the integration path may include peak contrast sensitivities (e.g., different peak sensitivities corresponding to different spatial frequencies) for all light adaptation levels in the (reference) dynamic range for the CSF.

[0055] As used herein, "integral path" may refer to a visible dynamic range (VDR) curve, which represents the nonlinearity of human perception and is used to establish a mapping between a set of digital code values ​​and a set of reference gray levels (quantized luminance values). The mapping may be required to satisfy the criterion that each quantization step (e.g., the luminance difference between two adjacent gray levels in Table 1) is less than the just-in-time difference (JND) above or below the corresponding light adaptation level (luminance value). The instantaneous derivative (in units of nits / spatial cycles) of the integral path at a particular light adaptation level (luminance value) is proportional to the JND at that particular adaptation level. As used herein, "VDR" or "visual dynamic range" may refer to a dynamic range wider than the standard dynamic range, including, but not limited to, the instantaneously perceptible dynamic range and color range that human vision can perceive at a given moment.

[0056] Based on the techniques described herein, a reference GSDF may be developed that is independent of any particular display or image processor. In some embodiments, one or more model parameters other than light adaptation level (luminance), spatial frequency, and angular size may be set to a constant (or fixed) value.

[0057] 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 high accuracy that exceeds the requirements of these display devices.

[0058] In some embodiments, the model parameters described herein are the field of view (FOV). 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 devices and viewing scenarios, including those used in studio, theater, or high-end entertainment systems.

[0059] The model parameters described herein may include an angular size parameter, which may relate to, for example, field of view. The angular size parameter may be set to values ​​such as 45 degrees by 45 degrees, 40 degrees by 40 degrees, 35 degrees by 35 degrees, 30 degrees by 30 degrees, 25 degrees by 25 degrees, or other larger or smaller values ​​to support studios, a 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 degrees by m degrees, where n and m may both be numbers between 30 and 40, and n and m may or may not be equal.

[0060] In some embodiments, a larger angular size (e.g., 40 degrees by 40 degrees) is used to produce a reference GSDF with a larger number of grayscale levels and therefore greater contrast sensitivity. The 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 widely varying viewing and display scenarios (e.g., movie theaters). While even larger angular sizes can be selected, the benefit of increasing the angular size significantly above a certain angular size (e.g., 40 degrees) may be relatively limited.

[0061] In some embodiments, the reference GSDF model covers a large luminance range. For example, the gray levels or quantized luminance values ​​represented by the reference GSDF model are 0 or approximately 0 (e.g., 10 -7 cd / m2 ) to 12,000 cd / m 2 The lower limit of the represented luminance values ​​in the reference GSDF model is 10 -7 cd / m 2 or lower or higher values ​​(e.g., 0, 10 -5 , 10 -8 , 10 -9 cd / m 2 The GSDF may be used to support a wide range of viewing and / or display scenarios with various ambient light levels. The GSDF may be used to support a wide range of display devices with various dark black levels (in theaters, indoors, or outdoors).

[0062] The upper limit of the luminance values ​​represented in the reference GSDF model is 12,000 cd / m 2 or lower or higher values ​​(e.g., 6000-8000, 8000-10000, 10000-12000, 12000-15000 cd / m 2 The GSDF may be used to support a wide range of viewing and / or display scenarios with high dynamic range. The GSDF may be used to support a wide range of display devices with different maximum luminance levels (HDR TVs, SDR displays, laptops, tablets, handheld devices, etc.).

[0063] 6. Variable Spatial Frequency 2 shows an example integration path (denoted VDR) that may be used to obtain the gray levels in the reference GSDF described herein, according to an example 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.

[0064] As shown in Figure 2, peak contrast sensitivity does not occur at a fixed spatial frequency value, but rather at smaller spatial frequencies as the light adaptation level (luminance value) decreases. This suggests that techniques with fixed spatial frequencies (e.g., DICOM) significantly exceed the contrast sensitivity of human vision for dark light adaptation levels (low luminance values). This means that there is a possibility of underestimating the contrast sensitivity. Lower contrast sensitivity leads to a higher contrast threshold, which in turn gives a larger quantization step in the quantized luminance values.

[0065] Unlike the Digital Imaging and Communications in Medicine (DICOM) standard, the VDR curve under the technique described in this paper does not fix spatial frequency model parameters to fixed values, such as every four cycles. Rather, the VDR curve varies with spatial frequency to accurately capture the peak contrast sensitivity of human vision at multiple light adaptation levels. The VDR curve properly takes into account the sharpening effect resulting from the adaptability of human vision to a wide range of light adaptation levels, helping to generate a highly accurate reference GSDF. Here, the term "high accuracy" means that perceptual errors due to the quantization of luminance values ​​are eliminated or substantially mitigated based on a reference GSDF that best and most efficiently captures the nonlinearities of human vision within the constraints of a fixed-size code space (e.g., one of 10 bits, 12 bits, etc.).

[0066] A calculation process may be used to calculate the gray levels in the reference GSDF (e.g., Table 1). In an exemplary embodiment, the calculation process is iterative or recursive, and derives a contrast threshold (or modulation threshold, e.g., m in Equation (4)) from the VDR curve. t ) is iteratively determined and the contrast threshold is applied to obtain a set of gray levels in the reference GSDF. This calculation process may be implemented using the following equation (7):

[0067]

number

[0068] Contrast threshold m related to JND t may be defined as a relative quantity, e.g., L max and L min The difference between L max or L min or L max and L min Between (for example, L max and L min , the average of m t is L max and L min The difference between L max or L min or L max and L min When quantizing luminance values ​​in the GSDF to multiple gray levels, L max and L min may refer to adjacent gray levels in the plurality of gray levels. j is expressed as m t Through L j-1 and L j+1 may be related to.

[0069] In alternative embodiments, instead of a linear expression such as that 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 the standard deviation divided by the mean may be used.

[0070] In some embodiments, the reference GSDF has a digital code value expressed as a 12-bit integer value ranging from 0 to 12,000 cd / m 2 To further improve the accuracy of the reference GSDF, t may be multiplied by a fraction value f. Furthermore, the central digital value L2048 (Note that digital code values ​​are limited to 0 and 4096, at least as in an SDI-compatible 12-bit code space) 2 Equation (7) may be mapped to the following equation (8):

[0071]

number

[0072] FIG. 3 illustrates an exemplary GSDF that maps between multiple gray levels (in logarithmic luminance values) and multiple digital code values ​​in a 12-bit code space in accordance with an exemplary embodiment of the present invention.

[0073] FIG. 4 shows a curve depicting the Weber fraction (Delta L / L or ΔL / L) based on gray levels for the example GSDF of FIG. 3. The perceptual nonlinearity of human vision shown in FIG. 4 is represented as a function of luminance value on a logarithmic luminance axis. Comparable visual differences (e.g., JNDs) in human vision correspond to larger Delta L / L values ​​at lower luminance values. The Weber fraction curve asymptotically approaches a constant value for higher luminance values ​​(e.g., a Weber fraction of 0.002, at which Weber's law is satisfied at higher luminance values).

[0074] 7. Function Model One or more analytical functions may be used to derive the mapping between digital code values ​​and gray levels in the GSDF described herein (a reference GSDF or a device-specific GSDF). The one or more analytical functions may be proprietary, standard-based, or a formula derived from standard-based functions. In some embodiments, a GSDF generator (e.g., 504 in FIG. 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 various image data codecs (e.g., 506 in FIG. 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 said analytical functions (whose coefficients are in integer or floating-point representation) may be provided directly to an image data codec or a wide variety of display devices for use in deriving the mapping between digital code values ​​and gray levels in the GSDF described herein and / or converting between gray levels and digital code levels for the purposes of encoding image data.

[0075] In some embodiments, the analytical functions described herein have a forward function that can be used to predict digital code values ​​based on corresponding gray levels, as follows:

[0076]

number

[0077] In some embodiments, the analytical function is an inverse function corresponding to the forward function in equation (9) and may be used to predict luminance values ​​based on corresponding digital code values, as follows:

[0078]

number

[0079] Similarly, luminance values ​​predicted based on multiple digital code values ​​using Equation (10) may be compared to observed luminance values. The observed luminance values ​​may be generated using, but are not limited to, numerical calculations based on the CSF model discussed above or using experimental data on human vision. In one embodiment, the deviation between the predicted and observed luminance values ​​may be derived as a function of the parameters n, m, c1, c2, and c3, and this deviation may be minimized to derive optimal values ​​for the parameters n, m, c1, c2, and c3 in Equation (10).

[0080] The set of optimal values ​​of the parameters n, m, c1, c2, and c3 determined using Equation (9) may or may not be the same as the set of optimal values ​​of the parameters n, m, c1, c2, and c3 determined using Equation (10). If there is a discrepancy between the two sets, one or both of the two sets may be used to generate a mapping between digital code values ​​and luminance values. In some embodiments, the two sets of optimal values ​​of the parameters n, m, c1, c2, and c3, if different, may be reconciled based on, for example, minimizing round trip error introduced by performing forward and reverse encoding operations according to both Equations (9) and (10). In some embodiments, multiple round trips may be performed to examine the resulting errors in the digital code values ​​and / or luminance values ​​or gray levels. In some embodiments, the selection of the parameters in Equations (9) and (10) may be based at least in part on the criterion that no significant error occurs in one, two, or more round trips. Examples of significant round-trip errors are 0.0001%, 0.001%, 0.01%, 0.1%, 1%, 2% or other configuration settings. It may include, but is not limited to, errors smaller than the possible values.

[0081] Some embodiments include using a code space of one of one or more different bit lengths to represent a digital control value. Optimized values ​​of the parameters in equations (9) and (10) may be obtained for each of the multiple code spaces, where each code space has a different one of one or more different bit lengths. Based on the optimized values ​​of equations (9) and (10), a code error distribution (e.g., a forward transform error, an inverse transform error, or a round-trip error in digital code values ​​based on equations (9) and (10)) may be determined. In some embodiments, a numerical difference of 1 between two digital code values ​​corresponds to a contrast threshold (or JND) in light levels between the two luminance values ​​represented by the two digital code values. FIG. 10A illustrates the maximum code error in JND units for multiple code spaces (with different bit lengths), each having a different one of one or more different precisions, according to some exemplary embodiments. For example, based on the functional model described herein, the maximum code error for a code space of infinite or unlimited bit length is 11.252. In contrast, based on the function model described in this paper, the maximum code error for a 12-bit (or 4096) code space is 11.298, which indicates that a 12-bit code space for digital code values ​​is a good choice for the function model represented by equations (9) and (10).

[0082] Figure 10B shows the distribution of code errors for a 12-bit long (or 4096) code space with a forward transform (from luminance values ​​to digital code values) specified by equation (9) according to an example embodiment. Figure 10C shows the distribution of code errors for a 12-bit long (or 4096) code space with a back transform (from digital code values ​​to luminance values) specified by equation (10) according to an example embodiment. Both Figures 10B and 10C show a maximum code error of less than 12.5.

[0083] 11 illustrates parameter values ​​that may be used in equations (9) and (10) according to an example embodiment. In some embodiments, as shown, integer-based formulas are used to represent / approximate these non-integer values ​​in specific implementations of the function models described herein. In other embodiments, fixed-point, 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.

[0084] Embodiments include using a functional model with a formula other than that given in equations (9) and (10), which may be a tone mapping curve. For example, a cone model with the Naka-Rushton formula as follows may be used with the functional model described herein:

[0085]

number

[0086] In another example, the function model may be generated with the Raised mu formula:

[0087]

number

[0088] As used herein, in some embodiments, a function model may be used to predict code values ​​from luminance values ​​or to predict luminance values ​​from code values. The formulas used by the function model may be invertible. 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 any exponents, may be expressed by fixed-point values ​​or integer-based formulas. In this manner, at least a portion of the processing logic may be efficiently implemented in hardware alone, software alone, or a combination of hardware and software. Similarly, at least a portion of the LUT generated by the function model or model formula (e.g., Equations (9)-(12)) may be efficiently implemented in hardware alone, software alone, or a combination of hardware and software (including an ASIC or FPGA). 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 error of the predicted code value may be within 14 code values ​​of the target or observed value across the full visible dynamic range of luminance values. In some embodiments, this is true for both the forward and inverse transforms. The same or different sets of model parameters may be used in the forward and inverse transforms. Round-trip accuracy may be maximized with optimal values ​​of the model parameters. Various code spaces may be used. In particular embodiments, a 12-bit wide (4096) code space may be used to accommodate digital code values ​​with minimal code error across the full visible dynamic range.

[0089] As used herein, a reference GSDF may refer to a GSDF that includes reference digital code values ​​and reference gray levels that are related under a functional model (whose model parameters may be determined with target or observed values ​​under a CSF model) that is determined numerically based on a CSF model (e.g., without determining any functional expression of the mapping between digital code values ​​and luminance values) or that is determined using data from human vision studies. In some embodiments, a device GSDF may also have a mapping between digital code values ​​and gray levels that may be analytically expressed by a functional model described herein.

[0090] 8. Image Data Exchange Based on Reference GSDF For purposes of illustration, the digital code values ​​have been described as existing in a 12-bit code space. However, the present invention is not so limited. Digital code values ​​having a different code space (e.g., a bit depth different from 12 bits) may be used in the reference GSDF. For example, 10-bit integer values ​​may be used to represent digital codes. In a 12-bit representation of a digital code, a digital code value of 4076 may be represented as a luminance value of 12000 cd / m 2 Instead, in the 10-bit representation of the digital code, the digital code value 1019 maps to a luminance value of 12000 cd / m 2 Thus, these and other variations in code space (bit depth) may be used for digital code values ​​in the reference GSDF.

[0091] A reference GSDF may be used to exchange image data across different GSDFs that may be designed individually 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 rely on model parameters that do not match those of the standard GSDF or the device-specific GSDF for another type of image acquisition or image rendering device.

[0092] The reference GSDF may correspond to the curve shapes depicted in Figures 3 and 4. In general, the shape of a GSDF depends on the parameters used to derive or design the GSDF. Thus, 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 curve shape of a device-specific GSDF depends on the particular device, including the display parameters and viewing conditions if the particular device is a display.

[0093] In one example, the supported luminance value range is 500 cd / m 2 A display limited to less than 0.05 may not experience the increased slope in high luminance value regions (which occurs when human vision shifts to logarithmic behavior for all frequencies) as shown in Figure 3. Driving this display with the curve shape of Figure 3 may lead to a non-optimal (e.g., less than optimal) allocation of gray levels, with too many gray levels allocated to light areas and not enough gray levels allocated to dark areas.

[0094] In another example, a low-contrast display is designed for use outdoors in a variety of daylight conditions. The luminance range of this display may lie largely, or almost entirely, in the logarithmic behavior region of Figure 3. Driving this low-contrast display with the curve shape of Figure 3 may again lead to a suboptimal (or worse) allocation of gray levels, with too many gray levels allocated to dark areas and not enough gray levels allocated to bright areas.

[0095] Under the techniques described herein, each display may use its own GSDF (which depends not only on the display parameters but also on the viewing conditions that affect, for example, the actual black level) to optimally support perceptual information in image data encoded with the reference GSDF. The reference GSDF is used by one or more upstream (e.g., encoding) devices for the overall encoding of the image data 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 particular devices that subsequently decode and / or render the image data.

[0096] Each device (e.g. display) supports / optimizes the device-specific gray levels. A display has a specific GSDF that is quantized into the device. The specific gray levels may be known to the display manufacturer, or the manufacturer may custom design the device to support a specific GSDF (which may or may not be standards-based). The device's line drivers may be implemented with device-specific quantized luminance values. For a device, optimization may best be 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 the lower limit of the device-specific gray level range may be set based in part 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 so set, the device-specific gray levels may be obtained or set by implicitly or explicitly accumulating (e.g., integrating) quantization steps in the device's line drivers. The derivation and / or adjustment of the gray levels may or may not be done at runtime while the device is concurrently rendering images.

[0097] Thus, under the techniques described herein, embodiments of the present invention may include, but are not limited to, encoding image data using a reference GSDF and decoding and rendering the image data using a display-specific GSDF.

[0098] The techniques described herein may be used to exchange image data across a variety of devices with different GSDFs. FIG. 5 illustrates an exemplary framework (500) for exchanging image data with devices with different GSDFs according to an exemplary embodiment of the present invention. As shown in FIG. 5, an adaptive CSF model (502) may be used to generate a reference GSDF (504). The term "adaptive" may refer to the ability of the CSF model to adapt to the nonlinearities and behavior of human vision. The adaptive CSF model may be constructed at least in part based on multiple CSF parameters (or model parameters). The multiple model parameters may include, for example, light adaptation level, viewing area width in degrees, noise level, accommodation (physical viewing distance), and luminance or color modulation vectors (which may, for example, relate to test images or image patterns used in the adaptive CSF model (502)).

[0099] An upstream (e.g., encoding) device may receive image data to be encoded using the reference GSDF (504) before transmitting or delivering the image data or a derivative thereof to a downstream (e.g., decoding) device. The image data to be encoded may initially be in any of several formats (standards-based, proprietary, extensions thereof, etc.) and / or may be derived from any of several image sources (cameras, image servers, tangible media, etc.). Examples of image data to be encoded include, but are not limited to, raw or other high-bit-depth image(s) 530. The raw or high-bit-depth images may originate from a camera, a studio system, an art director system, another upstream image processing system, an image server, a content database, etc. The image data may include, but is not limited to, digital photographs, video image frames, 3D images, non-3D images, computer-generated graphics, etc. The image data may include scene-based images, device-based images, or images with various dynamic ranges. Examples of image data to be encoded may include high-quality versions of original images that are to be edited, downsampled, and / or compressed, along with metadata, into coded bitstreams for delivery to image receiving systems (downstream image processing systems such as displays from various manufacturers). Raw or other high bit-depth images may be of high sampling rates used by professionals, art studios, broadcasters, high-end media production entities, etc. Image data to be encoded may be fully or partially encoded. They may be essentially computer-generated, or even derived, in whole or in part, from existing image sources such as old films and documentaries.

[0100] As used herein, the phrase "image data to be encoded" may refer to 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 an exemplary embodiment, the one or more images may be in an RGB color space. In another exemplary embodiment, the one or more images may be in a YUV color space. In one example, each pixel in an image described herein has floating-point pixel values ​​for all channels defined in the color space (e.g., red, green, and blue color channels in an RGB color space). In another example, each pixel in an image described herein has fixed-point pixel values ​​for all channels defined in the color space (e.g., 16-bit or more / fewer-bit fixed-point pixel values ​​for red, green, and blue color channels in an RGB color space). Each pixel may optionally and / or alternatively have downsampled pixel values ​​for one or more of the channels in the color space.

[0101] 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 to generate reference-encoded image data that is encoded using the reference digital code values ​​based on the image data to be encoded. The mapping operation from luminance values ​​based on the image data to be encoded to reference digital code values ​​may include selecting reference digital code values ​​whose corresponding reference gray levels (e.g., as shown in Table 1) more closely match or approximate the luminance values ​​specified by or determined from the image data to be encoded than any other reference luminance values ​​in the reference GSDF, and replacing the luminance values ​​with the reference digital code values ​​in the reference-encoded image data.

[0102] Additionally, optionally, or alternatively, 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.

[0103] In an example embodiment, the framework (500) may include software and / or hardware components (e.g., encoding or formatting unit (506)) configured to encode and / or format the reference-encoded image data into one or more coded bitstreams or image files. The coded bitstreams or image files may be in a standard-based format, a proprietary format, or an extended format based at least in part on a standard-based format. Additionally and / or optionally, the coded bitstreams or image files may include metadata including one or more of the reference GSDFs used to generate the reference-encoded image data, relevant parameters related to pre-processing or post-processing (e.g., model parameters; minimum luminance value, maximum luminance value, minimum digital code value, maximum digital code value, etc., as shown in Table 1, Figures 3 and 4; an identification field identifying a CSF among multiple CSFs; or a reference viewing distance).

[0104] In some embodiments, the framework (500) may have one or more separate upstream devices. For example, the one or more upstream devices in the framework (500) may be At least one of the upstream devices may be configured to encode image data based on the reference GSDF. The upstream device may have software and / or hardware components configured to perform functions related to 502, 504, and 506 of Figure 5. The encoded bitstream or image file may be output by the upstream device (502, 504, and 506 of Figure 5) through a network connection, a digital interface, a tangible storage medium, etc., and delivered in an image data flow (508) to another image processing device for processing or rendering.

[0105] In some exemplary embodiments, the framework (500) further includes one or more downstream devices as one or more separate devices. The downstream devices may be configured to receive / access the 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 include software and / or hardware components (e.g., a decoding or reformatting unit (510)) configured to decode and / or reformat the encoded bitstreams or image files to recover / obtain the reference encoded image data therein. As shown in FIG. 5, the downstream devices may include a variety of display devices.

[0106] 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 greater than or equal to that detectable by human vision.

[0107] In some embodiments, the display device's native digital code values ​​in the device-specific GSDF (which may be implemented as digitized voltage values ​​in the display system, e.g., digital drive levels or DDLs) may correspond to device-specific gray levels (or luminance values) that differ from those in the reference GSDF. The device-specific gray levels may be designed to support other standards, including sRGB, Rec. 709, or those using representations related to complementary densities. Additionally, optionally, or alternatively, the device-specific gray levels may be based on the intrinsic DAC characteristics of the display driver.

[0108] In some embodiments, display device A (512-A) may be designed and / or implemented to support a device-specific GSDF A (514-A) for a visible dynamic range (VDR) display. GSDF A (514-A) may be based on a 12-bit bit depth (12-bit code space) for device-specific digital code values, a contrast ratio (CR) of 10,000:1, and a >P3 color gamut. GSDF A (514-A) may be based on a first subrange (e.g., 0 to 5,000 cd / m) of the overall range of the reference GSDF (504). 2 ) within the entire range (e.g., 0 to 12,000 cd / m) within the reference GSDF (504). 2 ), but may have less than all of the reference gray levels in the reference GSDF (504).

[0109] In some embodiments, display device B (512-B) may be designed and / or implemented to support a device-specific GSDF B (514-B) for a dynamic range narrower than VDR. For example, display device B (512-B) may be a standard dynamic range (SDR) display. As used herein, 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, GSDF B (514-B) supports a device-specific bit depth of 8 bits for digital code values, a 500-5,000:1 conversion ratio, and a 100:1 conversion ratio. It may support the Trust Ratio (CR) and color ranges defined in Rec. 709. In some embodiments, GSDF B (514-B) is a second subrange (e.g., 0 to 2,000 cd / m) of the reference GSDF (504). 2 ) gray levels.

[0110] In some embodiments, the display device C (512-C) may be designed and / or implemented to support a device-specific GSDF C (514-C) for a dynamic range even narrower 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 a bit depth of 8 bits for device-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 (e.g., 0 to 1,200 cd / m) of the reference GSDF (504). 2 ) gray levels.

[0111] 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 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 (e.g., 0 to 100 cd / m) of the reference GSDF (504). 2 ) gray levels within the

[0112] The precision in image rendering may be gracefully scaled down for each of the display devices A through D (512-A through D). In some embodiments, a subset of the gray levels in each of the device-specific GSDFs A through D (514-A through D) may be correlated or mapped to the supported reference gray levels in the reference GSDF (504) in a manner that evenly distributes perceptually noticeable errors in the range of gray levels supported by that display device.

[0113] In some embodiments, a display device (e.g., one of 512-A-D) having a device-specific GSDF (e.g., one of 514-A-D) receives / extracts reference-encoded image data that has been encoded based on a reference GSDF. In response, the display device or a conversion unit (e.g., one of 516-A-D) therein maps 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 ​​includes 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 the reference digital code value) compared to any other device-specific gray level. In another example, mapping from the reference digital code value to the device-specific digital code value includes (1) determining a tone-mapped luminance value based on a reference gray level (corresponding to the reference digital code value) associated with the reference GSDF, and (2) selecting a device-specific gray level (corresponding to the device-specific digital code value) that closely matches or approximates the tone-mapped luminance value compared to any other device-specific gray level.

[0114] The display device or a driver chip therein (one of 518-A to -D) may then use the display-specific digital code values ​​to render an image using device-specific gray levels that correspond to the display-specific code values.

[0115] In general, the reference GSDF may be based on a different CSF model than the one on which the display-specific GSDF is based. A transformation / mapping between the reference GSDF and the device-specific GSDF is required. Even if the same CSF model is used to generate both the reference GSDF and the device-specific GSDF, different values ​​of the model parameters may have been used to derive those GSDFs. For the reference GSDF, the model parameter values ​​may be conservatively set 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 viewing conditions when the display device renders the image. Because 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, a transformation / mapping between the reference GSDF and the device-specific GSDF is still required. Here, the viewing condition parameters may include those that affect display quality (e.g., contrast ratio) and enhance the black level (e.g., minimum gray level). The conversion / mapping between the reference GSDF and the device-specific GSDF under the techniques described in this document improves quality in image rendering (e.g., improving contrast ratio by increasing luminance values ​​in high-value regions).

[0116] 9. Conversion of Reference-Encoded Image Data FIG. 6 illustrates an exemplary conversion unit (e.g., 516) according to some embodiments of the present invention. The conversion unit (516) may be, but is not limited to, one (e.g., 516-A) of the conversion units (e.g., 516-A to 516-D) illustrated in FIG. 5. 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 FIG. 5)). As used herein, "device-specific" and "display-specific" may be used interchangeably when the device is a display.

[0117] Based on the received definition data, the conversion unit (516) concatenates the reference GSDF with the display-specific GSDF to form a conversion lookup table (conversion LUT). The concatenation between the two GSDFs may include comparing gray levels in the two GSDFs and establishing a mapping between reference digital code values ​​in the reference GSDF and display-specific digital code values ​​in the display-specific GSDF based on the results of the gray level comparison.

[0118] More specifically, given a reference digital code value in the reference GSDF, its corresponding reference gray level may be determined based on the reference GSDF. The reference gray level so determined may be used to locate a device-specific gray level in the display-specific GSDF. In one exemplary embodiment, the located device-specific gray level may match or approximate the reference gray level more closely than 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 operating on the reference gray level, and the located device-specific gray level may match or approximate the tone-mapped luminance value more closely than any other display-specific gray level in the display-specific GSDF.

[0119] Using the device-specific gray level, the corresponding display-specific digital code value may be identified from the display-specific GSDF. In the transformation LUT, an entry may be added or defined consisting of the reference digital code value and the display-specific code value.

[0120] Each of the above steps may be repeated for other reference digital code values ​​in the reference GSDF. good.

[0121] In some embodiments, the transformation LUT may be pre-constructed and stored before image data whose processing is based at least in part 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 the reference digital code values ​​and the device-specific digital code values. For example, if the image data exhibits a particular concentration or distribution of luminance values, the transformation LUT may be set up to preserve a large amount of detail in areas of concentrated luminance values.

[0122] In some embodiments, the transformation unit (516) has one or more software and / or hardware components (comparison sub-units (602)) configured to compare quantization steps (e.g., luminance value differences or ΔL between adjacent digital code values) in both the reference GSDF and the display-specific GSDF (514-A). For example, the quantization step for a reference digital code value in the reference GSDF may be a reference luminance value difference (reference GSDF ΔL), while the quantization step for a display-specific digital code value in the display-specific GSDF may be a display-specific luminance value difference (display-specific GSDF ΔL), where the display-specific digital code value corresponds to (or is paired with in the transformation LUT unit) the reference digital code value. In some embodiments, the comparison sub-unit (602) compares these two luminance value differences. This process is essentially a test that may be performed based on the ΔL value, or optionally and / or alternatively based on the relative slopes of the two GSDF curves.

[0123] The quantization step for luminance values ​​in a display-specific GSDF is typically larger than that of the reference GSDF because one or more reference gray levels from the reference GSDF (e.g., corresponding to a high bit-depth region) are merged with a display-specific gray level from a display-specific GSDF (e.g., corresponding to a low bit-depth region). In such cases, dithering is used to remove banding artifacts. As part of the overall dithering, dithering is also performed on the local surrounding output pixels (in space and / or time). In a sense, the human eye may be represented as a low-pass filter. In at least this sense, averaging the local surrounding pixels as described herein produces a desired output gray level that reduces and / or eliminates banding visual artifacts that would otherwise be present due to the large quantization step in the display-specific GSDF.

[0124] In less common cases, the quantization step for luminance values ​​for the reference GSDF can sometimes be larger than that of the display-specific GSDF. A process based on a decontouring algorithm is used to synthesize an output gray level based on the input gray level, for example by averaging neighboring input pixels.

[0125] Correspondingly, in the case of the "Y" path in FIG. 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 transformation LUT that has the reference digital code value and the display-specific digital code value.

[0126] In the case of the "N" path in FIG. 6, where the reference GSDF ΔL is less than the display-specific GSDF ΔL, the dither algorithm flag is set for the entry in the transformation LUT that has the reference digital code value and the display-specific digital code value.

[0127] If the reference GSDF ΔL is equal to the display-specific GSDF ΔL, then neither the contour removal algorithm flag nor the dither algorithm flag is set for the entry in the transformation LUT that has the reference digital code value and the display-specific digital code value.

[0128] The contour removal and dither algorithm flags may be stored along with the entries in the transformation LUT, or may be stored in an associated data structure that is external to but operationally linked to the transformation LUT.

[0129] In some embodiments, the conversion unit (516) is configured to receive reference-encoded image data, which may be in the form of a high-bit-depth or floating-point input image, and to map reference digital code values ​​specified in the reference GSDF to display-specific digital code values ​​specified in the display-specific GSDF. In addition to mapping digital code values ​​between the GSDFs, the conversion unit (516) may be configured to perform edge removal or dithering based on the setting of the algorithm flags (edge ​​removal algorithm flag or dither algorithm flag) discussed above.

[0130] As previously mentioned, the reference GSDF is likely to contain a greater amount of detail than the display-specific GSDF. Thus, 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 transformation unit.

[0131] In some embodiments, given a reference digital code value determined for a pixel in the reference-encoded image data, the transformation unit (516) looks up a corresponding display-specific digital code value in a transformation LUT and replaces the reference digital code value with the corresponding display-specific digital code value. Additionally and / or optionally, the transformation unit (516) determines whether an edge removal or dither algorithm should be performed for the pixel based on the presence / setting of an algorithm flag for an entry in the transformation LUT that includes the reference digital code value and the display-specific digital code value.

[0132] If it is determined that neither the contour removal algorithm nor the dither algorithm should be performed (i.e., there is no instruction or flag to run either algorithm), then contour removal or dithering will not be performed on that pixel for the time being.

[0133] If it is determined that a decontour algorithm should be performed, the transformation unit (516) may perform one or more decontour algorithms. Performing the one or more decontour algorithms may include receiving image data of an input local neighborhood of pixels and inputting the image data of the local neighborhood of pixels into the decontour algorithm.

[0134] If it is determined that a dithering algorithm should be performed, the conversion unit (516) may perform one or more dithering algorithms.

[0135] The pixel may still be involved in contouring or dithering if the transformation unit (516) determines that contouring or dithering needs to be performed on the neighboring pixels. In one example, the device-specific (output) gray level of the pixel may be used to dither the local neighboring pixels. In another example, the reference (input) gray level of the pixel may be used to contour the local neighboring pixels. may be used.

[0136] In some embodiments, the conversion unit (516) outputs the results of the above steps to a downstream processing unit or sub-unit, where the 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).

[0137] FIG. 7 shows an exemplary SDR display (700) implementing 8-bit image processing. The SDR display (700) or a VDR decode unit therein receives an encoded input. The encoded input includes reference-encoded image data within an image data container, which may be one of several image data container formats. The VDR decode unit (702) decodes the encoded input to determine / obtain reference-encoded image data therefrom. The reference-encoded image data may include 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 reference digital code values ​​in a reference GSDF.

[0138] Additionally and / or optionally, the SDR display (700) includes a display management unit (704) that maintains display parameters for the SDR display (700). The display parameters may, at least in part, define a display-specific GSDF (e.g., GSDF-B in FIG. 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 primaries 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 preconfigured 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 a user of the SDR display (700). Some of the display parameters may be preconfigured with default values ​​and may be measured or overwritten by the user. The display management unit (704) may establish / shape perceptual nonlinearities 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 purposes of establishing / shaping perceptual nonlinearities of display-specific gray levels based on the reference GSDF, a transformation LUT such as that shown in FIG. 5 and / or other related metadata (e.g., dither and edge removal processing flags) may be established by the display management unit (704). A cascade of processes such as those discussed above may be implemented using the display management unit (704) to generate the transformation LUT and / or other related metadata (712) related to one or both of the reference GSDF and the display-specific GSDF.The transformation LUT and / or other related metadata (712) may be accessed and used by other units or subunits within the SDR display (700). Additionally, the transformation LUT and / or other related metadata may be used as or to derive metadata (714) for inverting perceptual nonlinearities. As used herein, inverting perceptual nonlinearities may include converting display-specific digital code values ​​to display-specific digital driving levels (e.g., digitized voltage levels at a display device).

[0139] Additionally and / or optionally, the SDR display (700) includes a transform unit (516) and an 8-bit perceptual quantizer (706) as shown in Figures 5 and 6. In some embodiments, the SDR display (700), or the transform unit (516) and 8-bit perceptual quantizer (706) therein, converts the reference-encoded image data into a display-specific bit-depth output image encoded with display-specific digital code values ​​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 herein, 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.

[0140] Additionally and / or optionally, the SDR display (700) includes a video post-processing unit (708) that may perform zero, one, or more image processing operations on the 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 portions of the SDR display (700).

[0141] In one exemplary embodiment, the SDR display (700) includes an 8-bit inverse perceptual quantizer (710) configured to convert display-specific digital code values ​​resulting from image processing operations into display-specific digital drive levels (e.g., digitized voltage levels). The display-specific digital drive levels generated by (or converted back from) the inverse perceptual quantizer (710) may support, among other things, 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 values ​​into display-specific digital drive levels that support luminance nonlinearities associated with Rec. 709. In another example, the inverse perceptual quantizer (710) converts the display-specific digital code values ​​into display-specific digital drive levels that support luminance nonlinearities associated with the linear or logarithmic luminance domains (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 values ​​into display-specific digital driving levels that have an optimal arrangement of display-specific gray levels for that particular display (700) and potentially support a display-specific CSF (or its associated GSDF) tailored for viewing conditions specific to that display (700).

[0142] 10. Exemplary Process Flow 8A illustrates an exemplary process flow according to one embodiment of the present invention. In some embodiments, one or more computing devices or components, such as one or more computing devices within framework 500, may perform this process flow. In block 802, a computing device receives image data to be encoded.

[0143] In block 804, the computing device encodes the image data to be encoded 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, where luminance values ​​in the image data to be encoded are represented by the set of reference digital code values, and a 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 a peak contrast sensitivity of adapted human vision at a certain light level.

[0144] In block 806, the computing device outputs the reference encoded image data.

[0145] In one embodiment, a computing device determines a reference grayscale display function (GSDF) based on a contrast sensitivity function (CSF), the reference GSDF specifying a reference mapping between the set of reference digital code values ​​and the set of reference gray levels. The CSF model includes one or more model parameters, which may have an angular size in a range including one or more of: between 25 degrees by 25 degrees and 30 degrees by 30 degrees, between 30 degrees by 30 degrees and 35 degrees by 35 degrees, between 35 degrees by 35 degrees and 40 degrees by 40 degrees, between 40 degrees by 40 degrees and 45 degrees by 45 degrees, or greater than 45 degrees by 45 degrees.

[0146] In one embodiment, the computing device derives a plurality of sub-mappings by assigning intermediate luminance values ​​within a range of luminance values ​​supported by the set of reference gray levels to intermediate digital code values ​​in a code space containing the set of reference digital code values ​​and performing one or more integration or integration calculations, each sub-mapping mapping a reference digital code value in the set of reference digital code values ​​to a reference gray level in the set of reference gray levels, the intermediate luminance values ​​may be selected within a range including one or more of less than 50 nits, between 50 and 100 nits, between 100 and 500 nits, or greater than 500 nits.

[0147] In certain exemplary embodiments, the set of reference gray levels covers a dynamic range with an upper limit having a value below 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 above 15000 nits.

[0148] In one embodiment, the peak contrast sensitivity is determined from a contrast sensitivity curve of a plurality of contrast sensitivity curves determined based on a contrast sensitivity function (CSF) model having model parameters including one or more of a luminance value variable, a spatial frequency variable, or one or more other variables.

[0149] In one embodiment, the at least two peak contrast sensitivities determined based on at least two of the plurality of contrast sensitivity curves occur at two different spatial frequency values.

[0150] In one embodiment, a computing device converts one or more input images represented, received, transmitted, or stored using image data to be encoded from an input video signal into one or more output images represented, received, transmitted, or stored using reference-encoded image data included in an output video signal.

[0151] In some embodiments, the image data to be encoded includes image data encoded in one of a high-resolution, high dynamic range (HDR) image format, an RGB color space associated with the Academy Color Encoding Specification (ACES) of the Academy of Motion Picture Arts and Sciences (AMPAS), the Digital Cinema Initiative's P3 color space standard, the Reference Input Medium Metric / Reference Output Medium Metric (RIMM / ROMM) standard, the sRGB color space, an RGB color space associated with the International Telecommunications Union (ITU) BT.709 Recommendation standard, or the like.

[0152] In one embodiment, the luminance difference between two reference gray levels represented by two adjacent reference digital code values ​​is less than the minimum noticeable difference threshold at that particular light level.

[0153] In an exemplary embodiment, the particular light level is a luminance value between (inclusive of) the two luminance values.

[0154] In some embodiments, the set of reference digital code values ​​has integer values ​​in a code space having a bit depth of less than 12 bits; between 12 bits and 14 bits; at least 14 bits; or 14 bits or more.

[0155] In some embodiments, the set of reference grey levels may comprise a set of quantized luminance values.

[0156] 8B illustrates another exemplary process flow according to an embodiment of the present invention. In some embodiments, one or more computing devices or components, such as one or more computing devices within 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.

[0157] In block 854, the computing device receives reference-encoded image data encoded with the set of reference digital code values. Luminance values ​​in the reference-encoded image data are based on the set of reference digital code values. A 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 a peak contrast sensitivity of adapted human vision at a particular light level.

[0158] In block 856, the computing device transcodes the reference-encoded image data encoded with the set of reference digital code values ​​into device-specific image data encoded with the device-specific digital control code based on the digital code mapping, wherein luminance values ​​in the device-specific image data are based on the set of device-specific digital code values.

[0159] In one embodiment, the computing device determines a set of correspondences between the set of reference digital code values ​​and the set of device-specific digital code values, where a correspondence in the set of correspondences relates a reference digital code value in the set of reference digital code values ​​to a device-specific digital code value. The computing device is further configured to compare a first luminance difference in the reference digital code values ​​with a second luminance difference in the device-specific digital code values, and store an algorithm flag indicating whether dithering, edge removal, or no operation should be performed on the reference digital code value based on the comparison of the first luminance difference and the second luminance difference.

[0160] In one embodiment, the computing device determines a reference digital code value from the reference encoded image data for a pixel and further determines whether an algorithm flag is set for the reference digital code value. In response to determining that the algorithm flag is set for dithering, the computing device performs a contour removal algorithm on the pixel. Alternatively, in response to determining that the algorithm flag is set for dithering, the computing device performs a dithering algorithm on the pixel.

[0161] In one embodiment, a computing device renders one or more images on a display based on device-specific image data encoded with the set of device-specific digital control codes, where the display may be one of, but is not limited to, a visible dynamic range (VDR) display, a standard dynamic range (SDR) display, a tablet computer display, or a handheld device display.

[0162] In one embodiment, a device-specific grayscale display function (GSDF) specifies a device-specific mapping between the set of device-specific digital code values ​​and the set of device-specific gray levels.

[0163] In one embodiment, the device-specific mapping is derived based on one or more display parameters and zero or more viewing condition parameters.

[0164] In some embodiments, the set of device-specific gray levels covers a dynamic range with an upper limit having a value below 100 nits, between 100 nits and 500 nits, between 500 nits and 1000 nits, between 1000 nits and 5000 nits, between 5000 nits and 10,000 nits, or above 10,000 nits.

[0165] In one embodiment, a computing device converts one or more input images represented, received, transmitted, or stored using reference-encoded image data from an input video signal into one or more output images represented, received, transmitted, or stored using device-specific image data included in an output video signal.

[0166] In some embodiments, the device-specific image data supports image rendering in one of a high-resolution, high-dynamic-range (HDR) image format, an RGB color space associated with the Academy Color Encoding Specification (ACES) of the Academy of Motion Picture Arts and Sciences (AMPAS), the Digital Cinema Initiative's P3 color space standard, the Reference Input Medium Metric / Reference Output Medium Metric (RIMM / ROMM) standard, the sRGB color space, and the RGB color space associated with the International Telecommunications Union's (ITU) BT.709 Recommendation standard.

[0167] 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.

[0168] In some embodiments, the set of device-specific gray levels may comprise a set of quantized luminance values.

[0169] In various embodiments, an encoder, decoder, system, etc. performs any or part of the methods described above.

[0170] 11. Mounting mechanism - Hardware overview According to one embodiment, the techniques described herein are implemented by one or more special-purpose computing devices. The special-purpose computing device may be hard-configured to perform the techniques, or may include one or more digital electronic devices, such as one or more application-specific integrated circuits (ASICs) or field-programmable gate arrays (FPGAs), that are permanently programmed to perform the techniques, or may include one or more general-purpose hardware processors that are programmed to perform the techniques according to program instructions in firmware, memory, other storage, or a combination. Such a special-purpose computing device may combine custom hard-configured logic, an ASIC, or an FPGA with custom programming to achieve the techniques. 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 hard-configured and / or program logic to implement the techniques.

[0171] 9 is a block diagram illustrating a computer system 900 upon which an exemplary embodiment of the present invention may be implemented. Computer system 900 includes a bus 902 or other communication mechanism for communicating information, and a hardware processor 904 coupled to bus 902 for processing information. Hardware processor 904 may be, for example, a general-purpose microprocessor.

[0172] Computer system 900 also includes a main memory 906, coupled to bus 902, such as a random access memory (RAM) or other dynamic storage device, for storing information and instructions to be executed by processor 904. Main memory 906 may also be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor 904. Such instructions, when stored in a non-transitory storage medium accessible to processor 904, render computer system 900 a special-purpose machine customized to perform the operations specified in the instructions.

[0173] Computer system 900 further includes a read only memory (ROM) 908 or other static storage device coupled to bus 902 for storing static information and instructions for processor 904. A storage device 910, such as a magnetic disk or optical disk, is provided and coupled to bus 902 for storing information and instructions.

[0174] Computer system 900 may be coupled via bus 902 to a display 912, such as a liquid crystal display, for displaying information to a computer user. An input device 914, including alphanumeric and other keys, is coupled to bus 902 for communicating information and command selections to processor 904. Another type of user input device is a cursor control 916, such as a mouse, trackball, or cursor direction keys, for communicating directional information and command selections to processor 904 and for controlling cursor movement on display 912. This input device typically has two degrees of freedom along two axes, a first axis (e.g., x) and a second axis (e.g., y), allowing the device to specify a position in a plane.

[0175] Computer system 900 may use customized fixed-configuration logic, one or more ASICs or FPGAs, firmware and / or program logic that, in combination with the computer system, configures or programs computer system 900 as a special purpose machine to implement the techniques described herein. According to one embodiment, the techniques of the present document are performed by computer system 900 in response to 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. The sequence of instructions contained in main memory 906 may be stored in a memory location 910. Execution of the sequence causes processor 904 to perform the process steps described herein. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions.

[0176] The term "storage medium" as used herein refers to any non-transitory medium that stores data and / or instructions that cause a machine to operate in a specific manner. Such storage media may include non-volatile media 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, solid-state drives, magnetic tape or any other magnetic data storage medium, CD-ROMs, any other optical data storage medium, any physical medium with a pattern of holes, RAM, PROMs and EPROMs, flash EPROMs, NVRAM, and any other memory chip or cartridge.

[0177] Storage media is distinct from, but may be used in conjunction with, transmission media. Transmission media participates in transferring information between storage media. For example, transmission media include coaxial cables, copper wire and fiber optics, including the wires that comprise bus 902. Transmission media can also take the form of acoustic or light waves, such as those generated during radio wave and infrared data communications.

[0178] Various forms of media may be involved in carrying one or more sequences of one or more instructions to processor 904 for execution. For example, the instructions may initially be carried on a magnetic disk or solid-state drive of a remote computer. The remote computer can load the instructions into its dynamic memory and send the instructions over a telephone line using a modem. A modem local to computer system 900 can receive the data on the telephone line and convert the data to an infrared signal using an infrared transmitter. An infrared detector can receive the data carried in the infrared signal and appropriate circuitry can place the data on bus 902. Bus 902 carries the data to main memory 906, from which processor 904 retrieves and executes the instructions. The instructions received by main memory 906 may optionally be stored on storage device 910 either before or after execution by processor 904.

[0179] Computer system 900 also includes a communication interface 918 coupled to bus 902. The communication interface 918 provides a two-way data communication coupling to a network link 920 that is connected to a local network 922. For example, communication interface 918 may be an Integrated Services Digital Network (ISDN) card, cable modem, satellite modem, or modem to provide a data communication connection to a corresponding type of telephone line. As another example, communication interface 918 may be a local area network (LAN) card to provide a data communication connection to a compatible LAN. Wireless links may also be implemented. In any such implementation, communication interface 918 sends and receives electrical, electromagnetic or optical signals that carry digital data streams representing various types of information.

[0180] Network link 920 typically provides data communication through one or more networks to other data devices. For example, network link 920 may provide a connection through local network 922 to a host computer 924 or to data equipment operated by an Internet Service Provider (ISP) 926. ISP 926 provides data communication services through the world-wide packet data communication network now commonly referred to as the "Internet" 928. Local network 922 and Internet 928 both use electrical, electromagnetic or optical signals that carry digital data streams. The signals through the various networks and the signals on network link 920 and through communication interface 918, which carry the digital data from, are example forms of transmission media.

[0181] Computer system 900 can send messages and receive data, including program code, through the network(s), network link 920 and communication interface 918. In the Internet example, a server 930 might transmit a requested code for an application program through Internet 928, ISP 926, local network 922 and communication interface 918.

[0182] The received code may be executed by processor 904 as it is received, and / or stored in storage device 910, or other non-volatile storage for later execution.

[0183] 12. Numbering Examples, Equivalents, Extensions, Alternatives, and Other 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 of different capabilities. As such, an embodiment of the present invention may relate to one or more of the examples listed in Table 2 below.

[0184] Table 2 Numbering example [EEE1] receiving image data to be encoded; encoding the 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 luminance values ​​in the received image data are represented by the set of reference digital code values, a 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 a peak contrast sensitivity of human vision adapted to a certain light level; outputting the reference encoded image data; method. [EEE2] The method of EEE1, further comprising determining a reference grayscale display function (GSDF) based on a contrast sensitivity function (CSF), the reference GSDF specifying the reference mapping between the set of reference digital code values ​​and the set of reference gray levels. [EEE3] 3. The method of claim EEE2, wherein the CSF model includes one or more model parameters having an angular size in a range including one or more of: between 25 degrees x 25 degrees and 30 degrees x 30 degrees, between 30 degrees x 30 degrees and 35 degrees x 35 degrees, between 35 degrees x 35 degrees and 40 degrees x 40 degrees, between 40 degrees x 40 degrees and 45 degrees x 45 degrees, or greater than 45 degrees x 45 degrees. [EEE4] The method of EEE1, comprising: assigning intermediate luminance values ​​within a range of luminance values ​​supported by said set of reference gray levels to intermediate digital code values ​​in a code space containing said set of reference digital code values; deriving a plurality of sub-mappings by performing one or more of an accumulation or integration calculation, each sub-mapping mapping a reference digital code value in the set of reference digital code values ​​to a reference gray level in the set of reference gray levels. method. [EEE5] 8. The method of claim 8, wherein the intermediate luminance value is selected within a range including one or more of: less than 50 nits, between 50 and 100 nits, between 100 and 500 nits, or greater than 500 nits. [EEE6] EEEL method, wherein the set of reference gray levels covers a dynamic range with an upper limit having a value less than 500 nits, between 500 and 1000 nits, between 1000 and 5000 nits, between 5000 and 10000 nits, between 10000 and 15000 nits, or greater than 15000 nits. [EEE7] The method of EEE1, wherein the peak contrast sensitivity is determined from a contrast sensitivity curve among a plurality of contrast sensitivity curves determined based on a contrast sensitivity function (CSF) model having model parameters including one or more of a luminance value variable, a spatial frequency variable, or one or more other variables. [EEE8] The method of EEE7, wherein the at least two peak contrast sensitivities determined based on at least two of the plurality of contrast sensitivity curves occur at two different spatial frequency values. [EEE9] The method of EEE1 further includes converting one or more input images represented, received, transmitted or stored using the image data to be encoded from an input video signal into one or more output images represented, received, transmitted or stored using the reference encoded image data included in an output video signal. [EEE10] The method of EEE1, wherein the image data to be encoded includes image data encoded in one of a high-resolution, high dynamic range (HDR) image format, an RGB color space associated with the Academy Color Encoding Standard (ACES) standard of the Academy of Motion Picture Arts and Sciences (AMPAS), the Digital Cinema Initiative's P3 color space standard, the Reference Input Medium Metric / Reference Output Medium Metric (RIMM / ROMM) standard, the sRGB color space, and an RGB color space associated with the International Telecommunications Union's (ITU) BT.709 Recommendation standard. [EEE11] The method of EEE1, wherein the luminance difference between two reference gray levels represented by two adjacent reference digital code values ​​is less than a just noticeable difference (JND) threshold at the particular light level. [EEE12] The method of EEE1, wherein the particular light level is a luminance value between (inclusive of) the two luminance values. [EEE13] The method of EEE1, wherein the set of reference digital code values ​​have integer values ​​in a code space having a bit depth of less than 12 bits; between 12 bits and 14 bits; at least 14 bits; or 14 bits or more. [EEE14] The method of EEE1, wherein the set of reference gray levels comprises a set of quantized luminance values. [EEE15] The method of EEE1, wherein the reference GSDF is determined based at least in part on a functional model expressed using one or more functions. [EEE16] The method of EEE15, wherein the functional model has one or more model parameters, and values ​​of the model parameters are optimized by minimizing deviation between predicted code values ​​and target code values. [EEE17] 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; receiving reference-encoded image data encoded with the set of reference digital code values, wherein luminance values ​​in the reference-encoded image data are based on the set of reference digital code values, and a 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 a peak contrast sensitivity of adapted human vision at a certain light level; transcoding the reference-encoded image data encoded with the set of reference digital code values ​​into device-specific image data encoded with the set of device-specific digital control codes based on the digital code mapping, wherein luminance values ​​in the device-specific image data are based on the set of device-specific digital code values. method. [EEE18] 1. The method according to EEE17, comprising: determining a set of correspondence relationships between the set of reference digital code values ​​and the set of device-specific digital code values, wherein a correspondence relationship in the set of correspondence relationships relates a reference digital code value in the set of reference digital code values ​​to a device-specific digital code value; comparing a first luminance difference in the reference digital code value with a second luminance difference in the device-specific digital code value; and storing an algorithm flag indicating whether dithering, contour removal, or no operation should be performed on the reference digital code value based on a comparison of the first luminance difference and the second luminance difference. method. [EEE19] determining a reference digital code value from the reference encoded image data for a pixel; and determining whether an algorithm flag is set for the reference digital code value. The method described in EEE17. [EEE20] The method as described in EEE19, further comprising, in response to determining that an algorithm flag is set for contour removal, running a contour removal algorithm on the pixel. [EEE21] responsive to determining that an algorithm flag for dithering is set, performing a dithering algorithm on the pixel. The method described. [EEE22] The method of EEE17, further comprising 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 of EEE17, wherein a device-specific Grayscale Display Function (GSDF) specifies the device-specific mapping between the set of device-specific digital code values ​​and the set of device-specific gray levels. [EEE24] The method of EEE17, wherein the device-specific mapping is derived based on one or more display parameters and zero or more viewing condition parameters. [EEE25] The method of EEE17, wherein the set of device-specific gray levels covers a dynamic range with an upper limit having a value less than 100 nits, greater than or equal to 100 nits but less than 500 nits, between 500 nits and 1000 nits, between 1000 nits and 5000 nits, between 5000 nits and 10,000 nits, or greater than 10,000 nits. [EEE26] The method of EEE17, further comprising converting one or more input images represented, received, transmitted or stored using the reference encoded image data from an input video signal into one or more output images represented, received, transmitted or stored using the device specific image data included in an output video signal. [EEE27] The method of EEE17, wherein the device-specific image data supports image rendering in one of a high-resolution, high-dynamic-range (HDR) image format, an RGB color space associated with the Academy Color Encoding Standard (ACES) standard of the Academy of Motion Picture Arts and Sciences (AMPAS), the Digital Cinema Initiative's P3 color space standard, the Reference Input Medium Metric / Reference Output Medium Metric (RIMM / ROMM) standard, the sRGB color space, and an RGB color space associated with the International Telecommunications Union's (ITU) BT.709 Recommendation standard. [EEE28] The method of EEE17, wherein the luminance difference between two reference gray levels represented by two adjacent reference digital code values ​​is less than a minimum noticeable difference threshold at the particular light level. [EEE29] The method of EEE17, wherein the particular light level is a luminance value between (inclusive of) the two luminance values. [EEE30] The method of 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 of EEE17, wherein the set of device-specific gray levels comprises a set of quantized luminance values. [EEE32] The method of EEE17, wherein at least one of the reference mapping and the device-specific mapping is determined based, at least in part, on a functional model expressed using one or more functions. [EEE33] The method of EEE32, wherein the functional model has one or more model parameters, and values ​​of the model parameters are optimized through minimizing deviation between predicted code values ​​and target code values. [EEE34] An encoder implementing the method of any one of EEE1 to EEE16. [EEE35] A decoder implementing the method of any one of claims EEE17 to EEE33. [EEE36] A system for carrying out the method of any one of EEE1 to EEE33. [EEE37] A system having an encoder and a decoder, The encoder: receiving image data to be encoded; encoding the 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 luminance values ​​in the image data to be encoded are represented by the set of reference digital code values, and a 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 a peak contrast sensitivity of human vision adapted at a certain light level; and outputting the reference encoded image data. The decoder: determining a digital code mapping between a set of reference digital code values ​​and a set of device-specific digital code values, the set of device-specific digital code values ​​being mapped to a set of device-specific gray levels in the device-specific mapping; receiving the reference encoded image data; and transcoding, based on the digital code mapping, the reference-encoded image data encoded with the set of reference digital code values ​​into device-specific image data encoded with the set of device-specific digital control codes, wherein luminance values ​​in the device-specific image data are based on the set of device-specific digital code values. system. [EEE38] 1. An image decoder comprising: a mapping determiner 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 that receives reference-encoded image data encoded with the set of reference digital code values, wherein luminance values ​​in the reference-encoded image data are based on the set of reference digital code values, and a 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 a peak contrast sensitivity of adapted human vision at a certain light level; a transcoder that transcodes the reference-encoded image data encoded with the set of reference digital code values ​​into device-specific image data encoded with the set of device-specific digital control codes based on the digital code mapping, wherein luminance values ​​in the device-specific image data are mapped to the set of device-specific digital code values; a transcoder based on the decoder. [EEE39] 1. A decoder according to EEE38, comprising: determining a set of correspondence relationships between the set of reference digital code values ​​and the set of device-specific digital code values, wherein a correspondence relationship in the set of correspondence relationships relates a reference digital code value in the set of reference digital code values ​​to a device-specific digital code value; comparing a first luminance difference in the reference digital code value with a second luminance difference in the device-specific digital code value; and storing an algorithm flag based on a comparison of the first luminance difference and the second luminance difference, the algorithm flag serving to flag whether dithering, edge removal, or no operation should be performed on the reference digital code value. decoder. [EEE40] determining a reference digital code value from the reference encoded image data for a pixel; and determining whether an algorithm flag is set for the reference digital code value. Decoder according to EEE38. [EEE41] The decoder of EEE40, further configured to, in response to determining that an algorithm flag for contour removal is set, perform a contour removal function on the pixel. [EEE42] The decoder of EEE40, further configured to, in response to determining that an algorithm flag for dithering is set, perform a dithering operation on the pixel. [EEE43] The decoder according to EEE38, 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 the set of device-specific digital code values ​​and the 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 viewing condition parameters. [EEE46] The decoder according to EEE38, wherein the set of device-specific gray levels spans (e.g., covers) a dynamic range with an upper limit having a value of less than 100 nits, greater than or equal to 100 nits 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] Receiving the reference encoded image data from the input video signal. 10. The decoder of claim 8, further comprising a converter that converts one or more input images, as received, transmitted or stored, into one or more output images, as represented, as received, transmitted or stored, using said device specific image data included in an output video signal. [EEE48] The decoder according to EEE38, wherein the device-specific image data supports image rendering in one of a high-resolution, high dynamic range (HDR) image format, an RGB color space associated with the Academy Color Encoding Standard (ACES) standard of the Academy of Motion Picture Arts and Sciences (AMPAS), a Digital Cinema Initiative P3 color space standard, a Reference Input Medium Metric / Reference Output Medium Metric (RIMM / ROMM) standard, an sRGB color space, and an RGB color space associated with the International Telecommunications Union (ITU) BT.709 Recommendation standard. [EEE49] The decoder according to EEE38, wherein the luminance difference between two reference grey levels represented by two adjacent reference digital code values ​​is less than a Just Noticeable Difference (JND) threshold at the particular light level. [EEE50] The decoder according to EEE38, wherein said particular light level is a luminance value between said two luminance values ​​inclusive. [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; more 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 comprises 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 functional model expressed using one or more functions. [EEE54] The decoder according to EEE53, wherein the functional model has one or more model parameters, the values ​​of which are optimized through minimizing the deviation between predicted code values ​​and target code values. [EEE55] A non-transitory computer-readable storage medium having encoded thereon instructions that, when executed by a computer or a processor thereof, cause or control or program the computer or processor to execute, perform or control a process for decoding an image, the image decoding process comprising: 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; receiving reference-encoded image data encoded with the set of reference digital code values, wherein luminance values ​​in the reference-encoded image data are based on the set of reference digital code values, and a 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 a peak contrast sensitivity of adapted human vision at a certain light level; Transcoding the reference encoded image data encoded with the set of reference digital code values ​​into device-specific image data encoded with the set of device-specific digital control codes based on the digital code mapping, wherein the luminance. [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; means for receiving reference-encoded image data encoded with the set of reference digital code values, wherein luminance values ​​in the reference-encoded image data are based on the set of reference digital code values, and a 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 a peak contrast sensitivity of adapted human vision at a certain light level; means for transcoding, based on the digital code mapping, the reference-encoded image data encoded with the set of reference digital code values ​​into device-specific image data encoded with the set of device-specific digital control codes, wherein luminance values ​​in the device-specific image data are based on the set of device-specific digital code values; Digital decoding system. [EEE57] receiving reference-encoded image data encoded with reference code values, the reference code values ​​representing a set of reference gray levels, a first pair of adjacent gray levels in the set of gray levels relating to a first peak contrast sensitivity of adapted human vision at a first light level, and a second pair of adjacent gray levels in the set of gray levels relating to a second peak contrast sensitivity of adapted human vision at a second, different light level; accessing a code mapping between reference code values ​​and device-specific code values, the device-specific code values ​​representing a set of device-specific gray levels; and transcoding the reference-encoded image data into device-specific image data encoded with the device-specific control code based on the code mapping. method. [EEE58] 8. The method of claim 8, wherein the set of reference gray levels covers a dynamic range with an upper limit having a value below 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 above 15000 nits. [EEE59] The method of EEE57, wherein the set of reference gray levels is constructed based on a human visual model that supports a field of view greater than 40 degrees. [EEE60] The method of EEE57, wherein the set of reference gray levels relates to varying spatial frequencies below a cutoff spatial frequency. [EEE61] The method according to EEE57, wherein the code mapping is configured to distribute perceptually noticeable errors evenly over the dynamic range covered by the device-specific gray levels. [EEE62] The method described in EEE57, wherein a first luminance value difference between the first pair of adjacent gray levels in the set of gray levels is inversely related to the first peak contrast sensitivity by a multiplicative constant, and a first luminance value difference between the second pair of adjacent gray levels is inversely related to the second peak contrast sensitivity by the same multiplicative constant. [EEE63] The method according to EEE57, wherein the reference code value and the reference gray level represented by the reference code value have different numerical values. [EEE64] 8. The method of claim 57, wherein the step of transcoding the reference encoded image data into device specific image data encoded with the device specific control codes based on the code mapping comprises: determining a first luminance value difference between two adjacent reference code values ​​at a reference code value; determining a second luminance value difference between two adjacent device specific code values ​​corresponding to the reference code value; applying one of a dithering algorithm or an edge removal algorithm to at least one pixel in the device-specific image data based on a comparison of the first luminance value difference and the second luminance value difference. method. [EEE65] 1. An image processing device comprising: a data receiver configured to receive reference encoded image data having reference code values, the reference encoded image data being encoded by an external encoding system, the reference code values ​​representing reference gray levels, the reference gray levels being selected using a reference grayscale display function based on perceptual nonlinearities of human vision adapted to spatial frequencies at various light levels; a data converter configured to access a code mapping between the reference code values ​​and device-specific code values ​​of the image processing device, the device-specific code values ​​being configured to produce device-specific gray levels configured for the image processing device, the data converter configured to transcode the reference-encoded image data into device-specific image data encoded with the device-specific code values ​​based on the code mapping; the image processing device is at least one of: a game console, a television, a laptop computer, a desktop computer, a netbook computer, a computer workstation, a cellular wireless telephone, an e-reader, a point-of-sale terminal, and a computer kiosk; Image processing device.

[0185] Table 3 below describes the calculation of the perceptual curve EOTF for converting digital video code values ​​to absolute linear luminance levels at the point of display. It also includes the inverse OETF for converting absolute linear luminance to digital code values.

[0186] Table 3. Example specifications for perceptual curve EOTF Example expression definition: 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≦10,000cd / m 2 Example EOTF Decoding Equation

number

number

number

[0187] Table 4 below shows exemplary values ​​for the 10 bits.

[0188] Table 4. Example table of values ​​for 10 bits

[0189] Table 2

[0190] Table 3

[0191] Table 4

[0192] Table 5

[0193] Table 6

[0194] Table 7

[0195] Table 8

[0196] Table 9

[0197] Table 10

[0198] Table 11

[0199] Table 12

[0200] In the foregoing specification, embodiments of the invention have been described with reference to numerous specific details that may vary depending on the implementation. As such, the sole and exclusive indication of what is, and what is intended by applicant to be, the invention is the claims of the patent issued on this application, in the particular form in which such claims are patented, including any subsequent amendments thereto. The definitions, if any, expressly set forth herein for terms contained in such claims will govern the meaning of such terms as used in the claims. Thus, no limitation, element, attribute, feature, advantage, or characteristic not expressly recited in a claim should in any way limit the scope of such claim. Accordingly, the specification and drawings are to be regarded in an illustrative and not restrictive sense.

[0201] Several aspects will be described. [Aspect 1] 1. An image processing device comprising: a data receiver configured to receive reference encoded image data having reference code values, the reference encoded image data being encoded by an external encoding system, the reference code values ​​representing reference gray levels, the reference gray levels being selected using a reference grayscale display function based on perceptual nonlinearities of human vision adapted to spatial frequencies at various light levels; a data converter configured to access a code mapping between the reference code values ​​and device-specific code values ​​of the image processing device, the device-specific code values ​​being configured to produce device-specific gray levels configured for the image processing device, the data converter configured to transcode the reference-encoded image data into device-specific image data encoded with the device-specific code values ​​based on the code mapping. Image processing device. [Aspect 2] 2. The image processing device of embodiment 1, wherein the image processing device is a standard dynamic range display device. Aspect 3 10. The image processing device of embodiment 1, configured to support one of an intermediate horizontal angle size between 40 degrees and 25 degrees and a narrow horizontal angle size of 25 degrees or less. Aspect 4 10. The image processing device of embodiment 1, configured to support one of an intermediate viewing distance of 1.5 meters to 0.5 meters and a short viewing distance of 0.5 meters or less. Aspect 5 receiving reference encoded image data encoded with reference code values, the reference code values ​​representing a set of reference gray levels; accessing a code mapping between reference code values ​​and device-specific code values, the device-specific code values ​​representing a set of device-specific gray levels; transcoding the reference-encoded image data into device-specific image data encoded with the device-specific control code based on the code mapping; a first quantization step between successive reference gray levels of the set is related to a contrast sensitivity of human vision adapted to a spatial frequency at a first light level; method. Aspect 6 6. The method of claim 5, wherein the first quantization step size is related to a peak contrast sensitivity of human vision adapted to a spatial frequency for the first light level. Aspect 7 6. The method of claim 5, wherein the first quantization step size is smaller than a minimum noticeable difference threshold at the first light level. Aspect 8 6. The method of embodiment 5, wherein the second quantization step is related to contrast sensitivity of human vision adapted to spatial frequency at a second light level, and wherein the first and second quantization step sizes are different. Aspect 9 9. The method of claim 8, wherein the second quantization step size is related to a peak contrast sensitivity of human vision adapted to a spatial frequency for the first light level. Aspect 10 9. The method of embodiment 8, wherein the first light level is less than the second light level and the first increment size is greater than the second increment size. Aspect 11 6. The method of embodiment 5, wherein the set of reference gray levels covers a dynamic range with an upper limit having a value between 1000 nits and 15000 nits.

Claims

1. An image processing device, a data receiver configured to receive reference encoded image data having reference code values, the reference encoded image data being encoded by an external encoding system, the reference code values ​​representing reference gray levels, the reference gray levels being selected using a reference grayscale display function based on perceptual nonlinearities of human vision adapted to spatial frequencies at various light levels; a data converter configured to access a code mapping between the reference code values ​​and device-specific code values ​​of the imaging device, the device-specific code values ​​being configured to produce device-specific gray levels configured for the imaging device, the data converter configured to transcode the reference-encoded image data into device-specific image data encoded with the device-specific code values ​​based on the code mapping; An image processing device comprising:

2. 1. A method for processing an image in an image processing device, comprising: receiving reference encoded image data having reference code values, the reference encoded image data being encoded by an external encoding system, the reference code values ​​representing reference gray levels, the reference gray levels being selected using a reference grayscale display function based on perceptual nonlinearities of human vision adapted to spatial frequencies at various light levels; accessing a code mapping between the reference code values ​​and device-specific code values ​​of the imaging device, the device-specific code values ​​being configured to produce device-specific gray levels configured for the imaging device, wherein accessing the code mapping transcodes the reference-encoded image data into device-specific image data encoded with the device-specific code values ​​based on the code mapping; A method for processing an image, including:

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