Real-time video dynamic range analysis

Abstract

A video analyzer measures and outputs a visual indication of a dynamic range of a video signal. The video analyzer includes a video input that receives the video signal, and a cumulative distribution function generator that generates a cumulative distribution function curve from a component of the video signal. A feature detector generates one or more feature vectors from the cumulative distribution function curve, and a video dynamic range generator produces a visual output that indicates a brightness of one or more portions of the video signal.

Description

Technical Field

[0001] This disclosure relates to systems and methods for measuring video signals, and particularly to the automatic analysis of the dynamic range of video signals. Background Technology

[0002] With the advent of 4K and 8K consumer displays, televisions (TVs) have seen rapid improvements in display size and resolution compared to the original 1080×1920 high-definition (HD) format, which can support content from streaming data services with 4K resolution. However, at typical viewing distances, these new high-resolution improvements for typical living room screen sizes may be difficult to perceive and fully appreciate, making further improvements in image resolution impractical.

[0003] Accordingly, advancements in video technology have focused on developing wider color gamuts (WCG), and especially on much wider contrast ratios and peak brightness high dynamic ranges (HDR) for modern displays, as these create very significant improvements in the viewer experience, which can be easily appreciated under typical living room viewing distances and lighting conditions.

[0004] HDR video content providers are rapidly converting classic film archives into a new HDR format for video DVDs and streaming services. These classic film archives have always had a greater dynamic range than standard dynamic range (SDR) video. Additionally, today's cameras have very large dynamic ranges, allowing for both live streaming and video production recording of HDR content, as well as broadcasting SDR content for those without HDR TVs.

[0005] Currently, there are three popular display formats and two WCG formats for HDR / WCG content. High-end studios use reference display monitors, or pixel monitors, which can directly display input in one or more of these formats.

[0006] HDR / WCG content originates from a wide range of camera, film, and digital archive formats that differ from the HDR / SDR pixel monitor input format. Consequently, adjustments are needed in red, green, and blue (RGB) brightness, contrast, color saturation, and gamma to compress or expand the dynamic range and color space to fit one of the pixel monitor formats.

[0007] The embodiments disclosed herein address these and other deficiencies of the prior art. Attached Figure Description

[0008] Referring to the accompanying drawings, aspects, features, and advantages of embodiments of the present disclosure will become apparent from the following description of the embodiments, wherein:

[0009] Figure 1This is a block diagram of a real-time video dynamic range analysis system according to an embodiment of the present disclosure.

[0010] Figure 2A and 2B It is for a specific video frame. Figure 1 Example output of a real-time video dynamic range analysis system.

[0011] Figure 3 It is for another specific video frame. Figure 1 Another example output from a real-time video dynamic range analysis system.

[0012] Figure 4 It is by Figure 1 The output of the real-time video dynamic range analysis system is the brightness curve of the video.

[0013] Figure 5 It is used to implement Figure 1 Examples of computer devices or systems for real-time video dynamic range analysis systems. Detailed Implementation

[0014] As mentioned above, high-end studio reference monitors, also referred to as pixel monitors in this article, can display input in one or more formats. Formats include, for example, the "gamma" format for HDR pixel monitors or the primary color format for WCG pixel monitors.

[0015] HDR pixel monitors display "gamma" formats can include perceptual quantization (PQ), graded from 1000 to 4000 nits peak brightness; mixed log-gamma is typically graded at 1000 nits peak brightness; and SRLive is typically graded at 1200 nits peak brightness. SCG pixel monitor primary color formats can include BT.2020, display primary colors with fully saturated "laser" light purity, which must be limited because current picture monitors cannot achieve pure RGB light, and P3-D65, which has a wide color gamut but lacks laser light purity in its achievable display primary colors.

[0016] HDR / WCG content originates from a wide range of camera, film, and digital archive formats that differ from the input format of HDR / SDR pixel monitors. Therefore, as mentioned above, adjustments are needed in RGB brightness, contrast, color saturation, and gamma to compress or expand the dynamic range and color space to suit one of the pixel monitor gamma formats.

[0017] Additionally, the brightness in the image should be quantified based on the display area to ensure that HDR / WCG content does not exceed the limitations of the target display technology. However, it may be difficult to measure the brightness of video images that may have very bright highlights that occupy only a small pixel area, or scenes that appear too dark for a TV in a not-very-dark room compared to a movie theater.

[0018] Modern TV pixel displays are limited in the display power or luminous intensity they can deliver on their very large screens. For example, a 70-inch diagonal TV or pixel monitor advertised as capable of displaying a peak brightness of 2000 nits (cd / m^2) may only be able to deliver that peak brightness over less than two percent of the screen area. In other words, a TV or pixel display cannot display a white field of 2000 nits across the entire screen because, given the display technology, this would consume far too much power.

[0019] Embodiments of this disclosure provide systems and methods for determining scene brightness as a function of image area, allowing adjustment of highlight content and average brightness when grading or downconverting HDR content in a target HDR display gamma format to SDR. Preferably, the system and method operate in real time for quality control checks and plotting of content across the entire video sequence.

[0020] Other aspects of embodiments of this disclosure provide real-time brightness markers associated with specific regions of interest in an image to quantify the brightness of desired midtones, specular highlights, and other regions of interest, such as skin tone exposure. In some embodiments, these markers may be selected based on pixel regions and have pixel region display indications, such as pseudo-color interpolation.

[0021] Other aspects of the embodiments may provide a method for determining whether a finite luminous intensity of a target pixel display monitor has been exceeded.

[0022] Figure 1 This is a block diagram of an example system 100 for real-time video dynamic range analysis according to some embodiments of the disclosed technology. System 100 can analyze video content in real time and determine scene brightness as a function of image area, allowing users to quickly identify whether adjustments should be made to highlight content and average brightness, while also grading or downconverting HDR content in HDR gamma format to SDR. The system can receive a Cb'Y'Cr' signal or an R'G'B' signal that can be converted into a luminance Y' signal in converter 102. Multiplexer 104 can select either the luminance or Y' component from the Cb'Y'Cr' signal or the converted R'G'B' signal.

[0023] Optional preprocessing 106 can be performed on the Y' component to resize it to a different format or to perform letterbox detection and cropping to the active pixels—either of both. The pixel count for subsequent processing only needs to be sufficient to provide a reasonable pixel count for the screen area, for example, as low as 0.01%. In terms of peak brightness, even on typical large screens, areas smaller than this are generally not of interest. For example, resizing a 4K or 2K image to 720×1280 still provides approximately 92 pixels for 0.01% of the area resolution.

[0024] Additionally, in some embodiments, system 100 may only be interested in the pixels of the active image, so any letter boxes (side panels or top / bottom black bands) should be detected and gated for subsequent pixel processing. After preprocessing 106, if the Y' component is an HDR Y' component, the SDR or HDR luminance component Y' can be sent to the SDR confidence monitor 108 via multiplexer 110 and lookup table 112. Lookup table 112 can downconvert the HDR Y' component to SDR, and the SDR confidence monitor 108 provides video image confidence monitoring capabilities that can be consistent with the real-time cumulative distribution function (CDF) or complementary cumulative distribution function (CCDF), which will be discussed in more detail below. This can be a panchromatic image or a monochrome image with pseudo-color to mark luminance areas.

[0025] In some embodiments, a 114-dimensional histogram is generated over the range of input data code values. The one-dimensional histogram is similar to a probability density function (PDF) and is a vector of pixel counts for each input code value on a single frame, and is then normalized to a fractional value with a unit cumulative sum.

[0026] In some embodiments, an optional recursive histogram filter 116 is applied to a one-dimensional histogram by providing a histogram vector with its own time average. For example, a recursive low-pass filter (LPF) is shown, whereby each bin of the histogram is averaged (autoregressive), where its values ​​from previous frames create a first-order exponential step response. A moving ensemble averaging time LPF with a finite impulse response can provide the same benefit.

[0027] The cumulative density function (CDF), also known as the cumulative histogram, is generated as a cumulative sum. Because the average histogram has a unit sum, the CDF will be a monotonically increasing function from zero to one, just like in traditional statistical functions.

[0028] Based on the output of CDF 118, feature detection 120 generates a set of real-time dynamic marker values, such as luminance, stop, and input code values ​​(CVs), at predetermined pixel regions in each frame by searching the CDF for the closest CV bin to each of the predetermined pixel probability sets. The predetermined pixel probability set is essentially a set of screen area fractions or percentages. Input CV values ​​can act as pseudo-color thresholds to indicate the predetermined pixel region for each marker on the pixel display in real time.

[0029] Based on the real-time dynamic tag value set, a CDF waveform can be plotted from the feature detection 120. The CDF waveform plots the pixel probability of the input CV relative to the pixel probability determined by the feature detection 120. Alternatively, the input CV can be converted to a full-scale percentage, and the pixel probability can be converted to nits or stops using a known gamma format of the video signal.

[0030] In some embodiments, a complementary CDF (CCDF) 122 can also be used to correlate the image area with the input CV. CCDF 122 is determined by subtracting the CDF from one, i.e., 1 – CDF = CCDF. CCDF 122 and the output of feature detection 120 are then received at the video dynamic range generator 124. The video dynamic range generator 124 generates a visual output indicating the brightness of one or more portions of the video signal in a form viewable by a user of system 100.

[0031] The video dynamic range generator 124 can produce visual output for a single frame 126 of the video input, which may include a drawn amount such as nits, stop, or input CV values ​​as a percentage of screen area or a log probability, as determined by feature detection 120. Because the input encoded gamma format is generally known, the CV scale output by CDF 118 or CCDF 120 can be converted to display light (nits) or scene reference (stop or reflection) for use in video production, quality control, and distribution workflows. The gamma format can be received from the metadata of the input signal or can be input by the user. The visual output of the signal frame allows the user to easily identify image areas that may contain HDR highlights exceeding typical display peak brightness, or content that may not be easily down-converted to SDR.

[0032] The video dynamic range generator 124 generates visual output in real-time or near real-time, meaning that visual output is generated as the video signal is being input to the system 100, or as quickly as possible after the signal is received to allow time for the creation of the visual output. In other embodiments, the output of the video dynamic range generator 124 may be temporarily stored for later analysis, or even stored indefinitely.

[0033] The video dynamic range generator 124 can also generate visual output for multiple frames 128 of the video input, which may include drawing nits relative to the frames, such as the average brightness of each frame, the maximum peak brightness of each frame, and the minimum brightness of each frame. The multi-frame output 128 can also allow the user to identify scenes with average brightness (large areas) that are either uncomfortably bright or uncomfortably dark for normal viewing conditions.

[0034] The video dynamic range generator 124 can additionally or alternatively generate a pseudo-image or pseudo-color image visual output for the video input frame by color encoding the image with luminance based on the amount of luminance present in different parts of the image. The pseudo-color image output 130 can visually correlate HDR highlights with real-time luminance nits values.

[0035] In some embodiments, a power limiting mask can also be determined for a particular display and simultaneously displayed on the visual output to allow the user to quickly and easily determine whether the input video has portions that are too bright or too dark for a particular display.

[0036] Display power limits can be determined using any known operation or method. When manufacturers specify maximum brightness in nits for a TV or monitor, they are typically specifying it for a specific area of ​​the TV or monitor screen.

[0037] In equation (1) below, the maximum brightness (Lmax) can be 2500, 1500, and 1000 nits, which are specified for less than 2% of the screen area for three different displays. Equation (2) states that 1 nit is equal to 1 candela per square meter.

[0038] Maximum display brightness

[0039]

[0040] 1 nit = 1 cd / m 2 (2).

[0041] For a 16x9 aspect ratio television or monitor, the diagonal length is 70 inches, and equation (3) converts the diagonal length D to meters.

[0042]

[0043] Equation (4) shown below then determines the area of ​​the television or monitor based on the diagonal length.

[0044]

[0045] Using equation (4) above, the area of ​​a 70-inch television or monitor is 1.341m². 2 .

[0046] In Equation 5, the peak brightness Plum is determined by multiplying the maximum brightness by 2% of the area.

[0047] Plum:=Lmax·Amax·0.02 (5).

[0048] For the three types of displays above, the peak brightness is shown in equation (6) below:

[0049]

[0050] Peak brightness, in candela, can be converted to nits, as shown in Equation (7).

[0051]

[0052] For the three types of displays discussed above, the results are shown in equation (8):

[0053]

[0054] In equation (9), the maximum number of nits is indicated by I, which is 10,000 in this example, i is from 0 to I-1, j is the screen area, which is 2% of the above, and the area of ​​each point i is determined by equation (9).

[0055]

[0056] Given the peak brightness in equation (5) above, the maximum brightness at each point can be determined using equation (1).

[0057]

[0058] max(Lum) = 2.5 x 10 3 min(Lum) = 20 (11)

[0059] Equations (9) to (11) are used to draw a power-limiting mask on the generated CDF or CCDF.

[0060] Figure 2A An exemplary CCDF waveform generated by system 100 operating on an example frame of an HDR PQ format input video is depicted. Figure 2B An example pseudo-color image generated by system 100 is illustrated. Although Figure 2B The pseudo-color image is shown in grayscale, but in reality, the pseudo-color image can be generated in color, where specific colors indicate various brightness or lightness levels of the original image. Figure 2A The figure shows a graph 200 illustrating the percentage of light displayed by a PQ HDR CCDF display on a video frame relative to the pixel area. This can be... Figure 1 An example of a single-frame view 126. A power limiting mask 202, such as the power limiting mask determined above using equations (9)-(11), is shown on a graph of a 1500-nit display. A CCDF waveform 204 is plotted on graph 200.

[0061] For ease of discussion, several markers are illustrated on graph 200, but all of these markers do not need to be shown in the CCDF waveform 204 when displayed to the user. Marker 206 illustrates 600 nits at 0.1% of the display area. Marker 208 illustrates 1% of the display area at 400 nits. Marker 210 illustrates 10% of the display area at 20 nits, and marker 212 illustrates 25% of the display area at 8 nits. As can be seen from marker 214, 3% of the display area is greater than 200 nits. Marker 216 illustrates the mean marker at 18 nits.

[0062] As can be seen in graph 200, Figure 2B 218 does not violate the 1500-nit area power limit mask. A color-coded key 220 can be provided to allow users to quickly identify which area of ​​the 218 pseudo-color image corresponds to which part of the CCDF waveform 204. Arrow 222 illustrates 0.1% of the area greater than 600 nits, while arrow 224 illustrates 1% of the area greater than 400 nits, and arrow 226 illustrates 3% of the HDR area greater than 200 nits.

[0063] Figure 3 Another example of the CCDF waveform 302 from another single frame of the video is illustrated. The power limiting mask 202 is also shown on graph 300. Labels 304, 306, 308, and 310 illustrate the number of nits on the CCDF waveform as area percentages of 0.1%, 1%, 10%, and 25%, respectively.

[0064] The mean value marker 312 is located at 112 nits. As can be seen at marker 308, the image has an area greater than 800 nits, exceeding 10% of the image, which exceeds the display power limit, as illustrated by the power limit mask 202. Additionally, an average brightness exceeding 100 nits (112 nits) could indicate an uncomfortablely bright image to most viewers. Despite... Figure 3 Not shown in the diagram, but the pseudo-color image 130 can also be displayed simultaneously with the curve graph 300 and the key, similar to the above. Figure 2A and 2B .

[0065] Figure 4 This is a graph 400 that can be displayed to the user, which uses system 100 to test whether video content will be fully displayed on a specific monitor. Graph 400 is... Figure 1 An example of a multi-frame view 128 generated by the video dynamic range generator 124. Graph 400 shows the average brightness waveform 402 of the video, the peak brightness of the waveform 404 (less than 1% of the image area), and the minimum brightness (peak black) of the waveform 406 (greater than 1% of the image area) measured by system 100 over the entire duration of the video input. The difference between the peaks represents the dynamic range of the darkest and brightest 1% across each frame of the video clip. Lines 408 and 410 indicate the maximum and minimum brightness of the display, respectively. Lines 412 and 414 indicate the optimal brightness zone of the display.

[0066] like Figure 4 As can be seen, the average brightness 402 is plotted across the entire 600 frames of video. The average brightness 402 remains within the monitor's maximum and minimum brightness range, as illustrated by lines 408 and 410, except for two excessively dark violations 416 and 418 around frames 100 and 170, respectively. Users of System 100 will be able to see these violations and adjust the video as needed to correct them, thus ensuring proper video display.

[0067] Figure 5 This is an illustration of elements or components that may exist in a computer device or system configured to implement methods, processes, functions, or operations according to embodiments of this disclosure. As noted, in some embodiments, the systems and methods described herein may be implemented in the form of means including processing elements and a set of executable instructions. The executable instructions may be part of a software application and arranged as a software architecture. Generally, embodiments of this disclosure may be implemented using a set of software instructions designed to be executed by appropriately programmed processing elements such as CPUs, microprocessors, processors, controllers, computing devices, etc. In complex applications or systems, such instructions are often arranged as “modules,” each of which typically performs a specific task, process, function, or operation. An operating system (OS) or other form of organizational platform may control or coordinate the entire set of modules in its operation.

[0068] Each application module or submodule may correspond to a specific function, method, process, or operation implemented by that module or submodule. Such functions, methods, processes, or operations may include functions, methods, processes, or operations for implementing one or more aspects of the systems and methods described herein.

[0069] Application modules and / or submodules may include any suitable computer-executable code or instruction set (e.g., as to be executed by a suitably programmed processor, microprocessor, or CPU), such as computer-executable code corresponding to a programming language. For example, programming language source code may be compiled into computer-executable code. Alternatively or additionally, the programming language may be an interpreted programming language, such as a scripting language. The computer-executable code or instruction set may be stored on (or on) any suitable non-transitory computer-readable medium. Generally, with respect to the embodiments described herein, a non-transitory computer-readable medium may include virtually any structure, technique, or method other than transient waveform or similar media.

[0070] As described, systems, apparatuses, methods, processes, functions, and / or operations for implementing embodiments of this disclosure can be implemented, in whole or in part, in the form of an instruction set executed by one or more programmed computer processors, such as central processing units (CPUs) or microprocessors. Such processors can be incorporated into apparatuses, servers, clients, or other computing or data processing devices that are operated by or communicate with other components of the system. As an example, Figure 5 It is a diagram illustrating elements or components that may exist in a computer device or system 500 configured to implement methods, processes, functions, or operations according to embodiments of the present disclosure. Figure 5 The subsystems shown are interconnected via system bus 502. The subsystems may include display 504 and peripherals, and I / O devices coupled to input / output (I / O) controller 506 can be connected to the computer system via any number of components known in the art, such as serial port 508. For example, serial port 508 or external interface 510 can be used to connect computer device 500 to... Figure 5 Other devices and / or systems, not shown, include wide area networks such as the Internet, mouse input devices, and / or scanners. Interconnection via system bus 502 allows one or more processors 512 to communicate with each subsystem and control the execution of instructions that may be stored in system memory 514 and / or fixed disk 516, as well as the exchange of information between subsystems. System memory 514 and / or fixed disk 516 may embody tangible computer-readable media. Figure 1 Any or all views 126, 128, 130 generated by the video dynamic range generator 124 can be displayed to the user of system 500 on display 504.

[0071] Any software component, process, or function described in this application can be implemented as software code executed by a processor using any suitable computer language employing techniques such as conventional or object-oriented methods, such as Java, JavaScript, C++, or Perl. The software code can be stored as a series of instructions or commands on (or on) a non-transitory computer-readable medium, such as random access memory (RAM), read-only memory (ROM), magnetic media such as hard disk drives or floppy disks, or optical media such as CD-ROMs. In this context, non-transitory computer-readable media are virtually any medium suitable for storing data or sets of instructions, except for transient waveforms. Any such computer-readable medium can reside on or within a single computing device and can exist on or within different computing devices within a system or network.

[0072] According to one example implementation, the term processing element or processor, as used herein, can be a central processing unit (CPU) or conceptualized as a CPU (such as a virtual machine). In this example implementation, the CPU or a device incorporating a CPU can be coupled, connected, and / or communicate with one or more peripheral devices, such as a display.

[0073] The non-transitory computer-readable storage media mentioned herein may include multiple physical drive units, such as a redundant array of independent disks (RAID), a floppy disk drive, flash memory, a USB flash drive, an external hard disk drive, a thumb drive, a pen drive, a key drive, a high-density digital versatile disc (HD-DVD) optical disc drive, an internal hard disk drive, a Blu-ray disc drive or a holographic digital data storage (HDDS) optical disc drive, synchronous dynamic random access memory (SDRAM) or similar devices or other forms of memory based on similar technologies. As mentioned, with respect to the embodiments described herein, non-transitory computer-readable media may include virtually any structure, technology, or method other than transient waveform or similar media.

[0074] This document describes certain implementations of the disclosed techniques with reference to system block diagrams and / or flowcharts or diagrams of functions, operations, processes, or methods. It will be understood that one or more blocks of a block diagram, or one or more stages or steps of a flowchart or diagram, and combinations of blocks in a block diagram and stages or steps in a flowchart or diagram, can be implemented by computer-executable program instructions, respectively. Note that in some embodiments, one or more blocks, stages, or steps may not necessarily need to be performed in the presented order, or may not need to be performed at all.

[0075] These computer-executable program instructions may be loaded onto a general-purpose computer, special-purpose computer, processor, or other programmable data processing apparatus to produce a particular example of a machine, such that the instructions, which are executable by the computer, processor, or other programmable data processing apparatus, create parts for implementing one or more of the functions, operations, processes, or methods described herein. These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of art including instruction parts that implement one or more of the functions, operations, processes, or methods described herein.

[0076] The aspects of this disclosure can operate on specially created hardware, firmware, digital signal processors, or on a specially programmed computer including a processor that operates according to programmed instructions. As used herein, the term controller or processor is intended to include microprocessors, microcomputers, application-specific integrated circuits (ASICs), and special-purpose hardware controllers. One or more aspects of this disclosure can be embodied in computer-usable data and computer-executable instructions, such as one or more program modules executed by one or more computers or other devices. Generally, program modules include routines, programs, objects, components, data structures, etc., which, when executed by a processor in a computer or other device, perform a specific task or implement a specific abstract data type. Computer-executable instructions can be stored on a computer-readable storage medium, such as a hard disk, optical disk, removable storage medium, solid-state memory, random access memory (RAM), etc. As those skilled in the art will appreciate, the functionality of a program module can be combined or distributed in various aspects as desired. Furthermore, functionality can be wholly or partially embodied in firmware or hardware equivalents such as integrated circuits, FPGAs, and the like. Specific data structures can be used to more efficiently implement one or more aspects of this disclosure, and such data structures are envisioned within the scope of computer-executable instructions and computer-available data described herein.

[0077] In some cases, the disclosed aspects may be implemented in hardware, firmware, software, or any combination thereof. The disclosed aspects may also be implemented as instructions carried on or stored thereon by one or more computer-readable storage media, which may be read and executed by one or more processors. Such instructions may be referred to as a computer program product. As discussed herein, a computer-readable medium means any medium that can be accessed by a computing device. By way of example and not limitation, a computer-readable medium may include computer storage media and communication media.

[0078] Computer storage media means any medium that can be used to store computer-readable information. By way of example and not limitation, computer storage media may include RAM, ROM, electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital video disc (DVD) or other optical disc storage devices, cassette tape, magnetic tape, disk storage devices or other magnetic storage devices, and any other volatile or non-volatile, removable or non-removable media implemented in any technology. Computer storage media excludes signals themselves and temporary forms of signal transmission.

[0079] Communication medium means any medium that can be used for communication of computer-readable information. By way of example and not limitation, communication medium may include coaxial cable, fiber optic cable, air, or any other medium suitable for communication of electrical, optical, radio frequency (RF), infrared, acoustic, or other types of signals.

[0080] Example

[0081] Illustrative examples of the techniques disclosed herein are provided below. Embodiments of the techniques may include any one or more of the examples described below, as well as any combination thereof.

[0082] Example 1 is a video analyzer for measuring the dynamic range of a video signal, comprising a video input configured to receive the video signal; a cumulative distribution function generator configured to generate cumulative distribution function curves from the components of the video signal; a feature detector configured to generate one or more feature vectors from the cumulative distribution function curves; and a video dynamic range generator configured to produce a visual output indicating the brightness of one or more portions of the video signal.

[0083] Example 2 is a video analyzer according to Example 1, wherein the visual output includes a pseudo-color image indicating the brightness of one or more portions of the video signal.

[0084] Example 3 is a video analyzer according to either Example 1 or 2, wherein the visual output includes a waveform indicating the percentage of brightness of a single frame of a video signal relative to the screen area.

[0085] Example 4 is a video analyzer based on Example 3, wherein the visual output includes a power mask for a specific display to be displayed simultaneously with the waveform.

[0086] Example 5 is a video analyzer according to any one of Examples 1 to 4, wherein the visual output includes the average brightness of each frame of at least a portion of the video signal.

[0087] Example 6 is a video analyzer based on Example 5, wherein the visual output further includes the maximum average brightness of a particular display.

[0088] Example 7 is a video analyzer based on Example 6, wherein the visual output further includes the minimum average brightness of a particular display.

[0089] Example 8 is a video analyzer based on Example 7, wherein the visual output further includes the optimal maximum brightness and optimal minimum brightness for a particular display.

[0090] Example 9 is a video analyzer based on any one of Examples 1 through 8, in which visual output is generated and displayed in real time or near real time.

[0091] Example 10 is a method for measuring the dynamic range of a video signal, comprising generating a cumulative distribution function curve from components of the video signal; generating one or more eigenvectors from the cumulative distribution function curve; and generating a visual output indicating the brightness of one or more portions of the video signal.

[0092] Example 11 is the method according to Example 10, wherein the visual output includes a pseudo-color image indicating the brightness of one or more portions of a video signal.

[0093] Example 12 is a method according to either Example 10 or 11, wherein the visual output includes a waveform indicating the brightness of a single frame of a video signal as a percentage of the screen area.

[0094] Example 13 is based on the method of Example 12, wherein the visual output includes a power mask of a specific display that is simultaneously displayed with the waveform.

[0095] Example 14 is a method according to any one of Examples 10 to 13, wherein the visual output includes the average brightness of each frame of at least a portion of the video signal.

[0096] Example 15 is based on the method of Example 14, wherein the visual output further includes the maximum average brightness and minimum average brightness of a particular display.

[0097] Example 16 is a method according to any one of Examples 10 to 15, wherein generating a visual output indicating the brightness of one or more portions of a video signal includes generating the visual output in real time or near real time.

[0098] Example 17 is one or more computer-readable storage media including instructions that, when executed by one or more processors of a video analyzer, cause the video analyzer to generate cumulative distribution function curves from components of a video signal; generate one or more feature vectors from the cumulative distribution function curves; and generate a visual output indicating the brightness of one or more portions of the video signal.

[0099] Example 18 is one or more computer-readable storage media according to Example 17, further including instructions to cause a video analyzer to generate visual output, said visual output including a pseudo-color image indicating the brightness of one or more portions of a video signal.

[0100] Example 19 is one or more computer-readable storage media according to any one of Examples 17 or 18, further including instructions to cause a video analyzer to generate visual output, said visual output including a waveform indicating the percentage of brightness of a single frame of a video signal relative to the screen area.

[0101] Example 20 is one or more computer-readable storage media according to any one of Examples 17 to 19, further including instructions for causing a video analyzer to generate visual output, the visual output including a power mask for a specific display to be displayed simultaneously with a waveform.

[0102] Example 21 is a video analyzer for measuring the dynamic range of a video signal, comprising a video input configured to receive a video signal; a cumulative distribution function generator configured to generate a cumulative distribution function curve from components of the video signal; a feature detector configured to generate one or more feature vectors from the cumulative distribution function curve; and a video dynamic range generator configured to generate a visual output indicating the brightness of one or more portions of the video signal from the cumulative distribution function curve and from the one or more feature vectors.

[0103] Example 22 is a method for measuring the dynamic range of a video signal, comprising generating a cumulative distribution function curve from components of the video signal; generating one or more eigenvectors from the cumulative distribution function curve; and generating a visual output from the cumulative distribution function curve and from the one or more eigenvectors indicating the brightness of one or more portions of the video signal.

[0104] Example 23 is one or more computer-readable storage media including instructions that, when executed by one or more processors of a video analyzer, cause the video analyzer to generate a cumulative distribution function curve from components of a video signal; generate one or more feature vectors from the cumulative distribution function curve; and generate a visual output from the cumulative distribution function curve and from the one or more feature vectors indicating the brightness of one or more portions of the video signal.

[0105] The previously described versions of the disclosed subject matter have numerous advantages, either as described or obvious to those skilled in the art. Even so, these advantages or features are not required in all versions of the disclosed apparatus, system, or method.

[0106] Additionally, this written description refers to specific features. It will be understood that the disclosure in this specification includes all possible combinations of those specific features. Where a specific feature is disclosed in the context of a particular aspect or example, that feature may also be used in the context of other aspects and examples to the greatest extent possible.

[0107] Furthermore, when referring to a method having two or more defined steps or operations in this application, the defined steps or operations may be performed in any order or simultaneously, unless the context precludes those possibilities.

[0108] Although specific examples of the invention have been illustrated and described for illustrative purposes, it will be understood that various modifications may be made without departing from the spirit and scope of the invention. Therefore, the invention should not be limited to anything other than the appended claims.

Claims

1. A video analyzer for measuring the dynamic range of a video signal, comprising: The video input is configured to receive video signals. The cumulative distribution function generator is constructed to generate cumulative distribution function curves from the components of the video signal; The feature detector is constructed to generate one or more feature vectors from the cumulative distribution function curve; as well as A video dynamic range generator is configured to generate a visual output indicating the brightness of one or more portions of a video signal based on the one or more feature vectors.

2. The video analyzer of claim 1, wherein the visual output comprises a pseudo-color image indicating the brightness of one or more portions of the video signal.

3. The video analyzer of claim 1, wherein the visual output includes a waveform indicating the percentage of brightness of a single frame of the video signal relative to the screen area.

4. The video analyzer of claim 3, wherein the visual output includes a power mask for a specific display to be displayed simultaneously with the waveform.

5. The video analyzer of claim 1, wherein the visual output comprises the average brightness of each frame of at least a portion of the video signal.

6. The video analyzer of claim 5, wherein the visual output further includes the maximum average brightness of a particular display.

7. The video analyzer of claim 6, wherein the visual output further includes the minimum average brightness of a particular display.

8. The video analyzer of claim 7, wherein the visual output further comprises an optimal maximum brightness and an optimal minimum brightness for a particular display.

9. The video analyzer of claim 1, wherein visual output is generated and displayed in real time or near real time.

10. A method for measuring the dynamic range of a video signal, comprising: Generate cumulative distribution function curves from the components of the video signal; Generate one or more feature vectors from the cumulative distribution function curve; as well as A visual output indicating the brightness of one or more portions of a video signal is generated based on the one or more feature vectors.

11. The method of claim 10, wherein the visual output comprises a pseudo-color image indicating the brightness of one or more portions of a video signal.

12. The method of claim 10, wherein the visual output comprises a waveform indicating the percentage of brightness of a single frame of a video signal relative to the screen area.

13. The method of claim 12, wherein the visual output includes a power mask of a specific display that is simultaneously displayed with the waveform.

14. The method of claim 10, wherein the visual output comprises the average brightness of each frame of at least a portion of the video signal.

15. The method of claim 14, wherein the visual output further comprises a maximum average brightness and a minimum average brightness of a particular display.

16. The method of claim 10, wherein generating a visual output indicating the brightness of one or more portions of a video signal comprises generating the visual output in real time or near real time.

17. One or more computer-readable storage media including instructions that, when executed by one or more processors of a video analyzer, cause the video analyzer to perform the following operations: Generate cumulative distribution function curves from the components of the video signal; Generate one or more eigenvectors from the cumulative distribution function curve; and A visual output indicating the brightness of one or more portions of the video signal is generated based on the one or more feature vectors.

18. The one or more computer-readable storage media of claim 17, further comprising instructions for causing a video analyzer to generate visual output, said visual output comprising a pseudo-color image indicating the brightness of one or more portions of a video signal.

19. The one or more computer-readable storage media of claim 17, further comprising instructions for causing a video analyzer to generate visual output, said visual output comprising a waveform indicating the percentage of brightness of a single frame of a video signal relative to the screen area.

20. The one or more computer-readable storage media of claim 19, further comprising instructions for causing a video analyzer to generate visual output, said visual output including a power mask for a specific display to be displayed simultaneously with a waveform.

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