Image processing system, method, and apparatus

Abstract

1. A method for reducing light contamination in a displayed image, the method comprising: receiving a portion of a scene to be displayed on a display at an initial brightness; adjusting the initial brightness of the portion of the scene to a final brightness based on a light contamination model associated with the display; and rendering the scene on the display with the portion of the scene having the final brightness.

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to and the benefit of U.S. Provisional Patent Application No. 63 / 357,279, filed June 30, 2022, the entire contents of which are incorporated herein by reference.

[0002] FIELD OF THE DISCLOSURE This disclosure relates generally to image processing systems, methods, and apparatus for reducing light contamination, for example, in a displayed image or scene. [Background technology]

[0003] The quality of an image displayed on a display depends on the capabilities of the hardware and / or software of the display system. Some characteristics that affect the quality of an image displayed on a display include resolution, contrast ratio, brightness, etc. Image processing techniques can be used to adjust the characteristics of the image to improve the quality of the image when displayed on a display. Summary of the Invention

[0004] Additional features and advantages are described herein, and will be apparent from the following description and drawings.

[0005] At least one embodiment of the present disclosure relates to a method for reducing light contamination in a displayed image. The method may include receiving a portion of a scene to be displayed on a display at an initial luminance, adjusting the initial luminance of the portion of the scene to a final luminance based on a light contamination model associated with the display, and rendering the scene on the display with the portion of the scene having the final luminance.

[0006] At least one embodiment of the present disclosure relates to an apparatus for reducing light contamination in a displayed image, the apparatus may include a processing circuit that receives a portion of a scene to be displayed on a display at an initial brightness, adjusts the initial brightness of the portion of the scene to a final brightness based on a light contamination model associated with the display, and renders the scene on the display with the portion of the scene having the final brightness.

[0007] At least one embodiment of the present disclosure relates to a system for reducing light contamination in a displayed image, the system may include a processing circuit that determines an initial luminance of pixels in each of a plurality of frames of a video signal to be displayed on a display, adjusts, for each frame, the initial luminance of the pixels in each frame to a final luminance based on a light contamination model that compensates for light contamination that occurs when each frame is displayed on the display, and renders each frame on the display using pixels having the final luminance.

[0008] At least one embodiment of the present disclosure relates to a method for calibrating a display system, the method including displaying a calibration image on a display, capturing the calibration image displayed on the display using one or more image sensors, determining light contamination of zones of the calibration image based on the captured calibration image, and generating a light contamination model based on the determined light contamination for the zones.

[0009] At least one embodiment of the present disclosure relates to a system including a screen, one or more projectors that project images onto the screen, one or more image sensors, and an image processing circuit, wherein the image processing circuit controls the one or more image sensors to render a calibration image onto the screen using the one or more projectors and capture the calibration image displayed on the screen, determine light contamination of a zone of the calibration image based on the captured calibration image, and generate a light contamination model based on the determined light contamination of the zone.

[0010] The present disclosure is described in conjunction with the accompanying drawings, which are not necessarily drawn to scale. [Brief explanation of the drawings]

[0011] [Figure 1] FIG. 1 illustrates a display system in accordance with at least one example embodiment. [Figure 2] FIG. 2 illustrates various exemplary implementations of the display system of FIG. [Figure 3] FIG. 3 illustrates an example pattern for a calibration image in accordance with at least one example embodiment. [Figure 4] FIG. 4 illustrates a method in accordance with at least one example embodiment. [Figure 5] FIG. 5 illustrates a method in accordance with at least one example embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0012] The following description provides embodiments only and is not intended to limit the scope, applicability, or configuration of the claims. Rather, the following description provides one of ordinary skill in the art with an enabling description of implementing the described embodiments. It should be understood that various changes can be made in the function and arrangement of elements without departing from the spirit and scope of the appended claims.

[0013] It will be appreciated from the following description, and for reasons of computational efficiency, that components of the system may be located in any suitable location within a distributed network of components without affecting the operation of the system.

[0014] Furthermore, it should be understood that the various links connecting the elements may be wire, trace, or wireless links, or any suitable combination thereof, or any other suitable known or later developed element capable of providing and / or communicating data to and from the connected elements. For example, the transmission medium used as the link may be any suitable carrier for electrical signals, including coaxial cable, copper wire and optical fiber, electrical traces on a PCB, etc.

[0015] As used herein, the words "at least one," "one or more," "or," and "and / or" are open-ended expressions that are both conjunctive and disjunctive in operation. For example, the phrases "at least one of A, B, and C," "at least one of A, B, or C," "one or more of A, B, and C," "one or more of A, B, or C," "A, B, and / or C," and "A, B, or C" each mean A alone, B alone, C alone, A and B together, A and C together, B and C together, or A, B, and C together.

[0016] As used herein, the terms “determine,” “calculate,” and “compute,” as well as variations thereof, are used interchangeably and include any suitable type of methodology, process, operation, or technique.

[0017] Various aspects of the present disclosure are described herein with reference to drawings that may be schematic illustrations of idealized configurations.

[0018] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. It will be further understood that terms such as those defined in commonly used dictionaries should be interpreted to have a meaning consistent with their meaning in the context of the relevant art and this disclosure.

[0019] As used herein, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly dictates otherwise. It will be further understood that the terms "include," "including," "includes," "comprise," "comprises," and / or "comprising," as used herein, specify the presence of stated features, integers, stages, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, stages, operations, elements, components, and / or groups thereof. The term "and / or" includes any and all combinations of one or more of the associated listed items.

[0020] Where reference to a general element or set of elements instead of a specific element is appropriate, the description may refer to the element or set of elements by its root term. For example, where reference to a specific element Xa, Xb, Xc, etc. is not necessary, the description may be referred to generally as "X."

[0021] Exemplary embodiments generally relate to predicting and overcoming fill-factor-dependent contrast loss in displayed images. The instantaneous range of displayable luminance, also known as the system contrast ratio (CR) provided at the eyepoint (EP) of a collimated Level-D flight simulator, is determined by numerous optical and geometric limitations. While these limitations are often necessary compromises to achieve a desired field of view (FOV), the peak luminance (also known as white level) and luminance uniformity, the magnitude of CR reduction, and the magnitude of subsequent grayscale distortion, also known as "washout," are determined by the relative luminance of the displayed FOV, also known as the fill factor (FF). Exemplary embodiments provide a process for estimating, for a given input image, the impact of FF on the system CR and the corresponding washout of grayscales near the system black level that can be obtained in a simulated washed-out version of the input image. The just-noticeable difference (JND) between the displayed luminance level and the system black level is then estimated, and the washed-out image is analyzed. The washout creates significant areas of visual indistinguishability and significant areas of grayscale nonlinearity. To recapture and realign these lost and distorted grayscales, a system-level solution is proposed. The system approach introduces a dynamic FF-dependent Tone Map (TM) operator that is uniquely enabled by the Image Generator (IG) or Image Processor.

[0022] Projector contrast ratio CR (CR PJ) ranges from 1000:1 to 500,000:1, while system CR is on the order of 10:1. The difference between these measurements is the fill factor (FF) of the applied test pattern. Projector CR is commonly referred to in terms of its "continuous" CR, where the luminance of a full-white and full-black pattern is measured sequentially and the results are divided. System CR, however, is measured using a checkerboard of white and black squares. For a given image commanded by IG and then converted to grayscale, FF is defined as the sum of the commanded grayscale values from all pixels divided by the same sum for the all-white image. FF may range from 0.0 to 1.0, depending on the test pattern. Given this definition, sequential CR measurements represent the limiting case where FF approaches 0 and CR is maximized. However, a standard checkerboard pattern (an image of equal-sized half-black and half-white blocks) will produce a relative command luminance of 0.5 and equivalently a 0.5 (or CR) 0.5 ) This higher fill factor provides a significantly lower CR.

[0023] A number of factors limit the CR efficiency of a collimated display system. For a typical rear-projected Level-D flight simulation system, the limiting factors encountered from image creation to image observation include image generator capabilities, alignment system capabilities, projector capabilities and native contrast, imaging optics (lens) efficiency, projection screen-related parameters such as specular and diffuse reflection, ambient light, and human visual capabilities.

[0024] As FF increases, the amount of light in the system increases, increasing the magnitude of light scattered and reflected from components between the IG (e.g., the projector's imaging panel) and the eyepoint. While some of this scattered and reflected light can be reduced or eliminated by careful system layout and optically absorbing baffles around the projection screen and lens, a significant portion cannot be captured without degrading important system performance parameters such as FOV and brightness uniformity. Some of this unblocked light strikes the imaging surface of the projection screen, resulting in undesirable light leakage L. N causes.

[0025] To provide adequate brightness uniformity for an image composed of light projected from a wide variety of angles, the projection screen of a typical collimated system has a relatively wide scattering and reflection profile. These profiles tend to capture a wide distribution of desired and unwanted light. As a result, it can be reasonably assumed that unwanted light from a given pixel will be distributed approximately uniformly across all other pixels in the FOV. This allows the system black level to be adjusted to L N increases uniformly by

[0026] The assumption of uniform scattering greatly simplifies the calculation of the system CR for a given FF. This is shown in the following equation, where L W and L b represent the white level and black level of the projector, respectively. Next, for the test pattern shown in Figure 3, the white level and black level measured at the eye point are respectively (L W +L N ) and (L b +L N ) TIFF2025525461000002.tif10150

[0027] In fact, the numerator and denominator L NThe terms are not perfectly equal, and their ratio determines the degree to which the unwanted scattered light is uniformly distributed. Thus, the ratio of these terms should provide a correlation metric that indicates the degree to which the uniform scattering model is successful in representing the system contrast in a given application.

[0028] The wide scattering and reflection profiles used in collimated display systems ensure that even complex images with unbalanced brightness distributions are uniformly scattered to produce a uniform L. N An exemplary embodiment relating to the generation of a light contamination model demonstrates that the calculated FF for a given FOV in a collimated display system reduces L by approximately the same amount as a black and white test pattern with the same FF. N This allows for the straightforward collection of validation data to verify the applicability of the model, as provided in the following section.

[0029] The formula representation of the FF dependency system CR is shown below, where C sys L N is the contrast coefficient that uniquely determines the magnitude of the CR limit by adjusting for the contribution from the ambient luminance (L A ) is L w or L b For collimated display systems, the ambient luminance L A is usually negligible. Generally speaking, ambient brightness may be more important for large FOV systems. TIFF2025525461000003.tif10150 TIFF2025525461000004.tif11150

[0030] Projector (CR pj ) can be assumed to represent the relationship between the input Lw and Lb values. TIFF2025525461000005.tif10150

[0031] Therefore, the contrast coefficient Csys The system checkerboard CR,CR 0.5 This is very useful because the checkerboard CR of the display system is routinely known or measured. TIFF2025525461000006.tif11150

[0032] Therefore, the system CR as a function of the FF for a particular system is, within the stated approximation, CR PJ , C.R. 0.5 , and L if necessary A can be uniquely determined by

[0033] Although this model supports the prediction that system CR is dramatically affected by FF, FF values for common flight training tasks conducted in flight simulators are still largely unknown. The relationship between FF and time of day (TOD) for a given scene is useful for understanding the impact of FF on flight training tasks.

[0034] Calculating the system CR provides a reasonable estimate of the FF, which represents the entire system FOV. The following description, in some cases, describes a partial FOV provided by a single projection channel (single projector). The examples described herein were performed with a 180° horizontal and 40° vertical FOV, representing the Level-D standard for flight simulators.

[0035] A pilot's ability to identify and ultimately discriminate task-critical surfaces and objects is determined in large part by the system CR. During near-ground maneuvers such as takeoff and landing, adequate system contrast is especially important for discrimination of light points, runway markings, and related displays. At decision height (DH), adequate system contrast can mean the difference between a safe landing and a failed approach. The table below shows estimated FF ranges by TOD at DH for 64 combinations of various luminances, celestial spheres, and weather controls. TIFF2025525461000007.tif41141

[0036] The corresponding system CR estimates provided in the table above imply that nighttime scenes benefit from the relatively high sequential CR provided by the projector. For other TODs, unwanted light added to the imaging surface (i.e., the projection screen) significantly exceeds the projector's initial black level. As a result, increases in projector sequential CR are likely only observable for nighttime scenes. For DH applications, the system CR for dawn, dusk, and daytime scenarios can be significantly improved by increasing the display system CR, most likely achieved by enhancing the CR of the projection screen.

[0037] As FF increases, the system CR decreases and the system black level increases. If the image generator does not compensate for the system's dynamic CR and estimates a high static CR, such as the sequential CR of a projector, this increase in black level can make content in very dark scenes invisible. Content above this level also suffers from unintended tone compression, which distorts the intended tone map (TM) operator and creates visual nonlinearities in the perceived gray levels. This effect, the loss of dark tones and the nonlinear distortion of the upper levels, is called system washout. System washout is driven by the system CR and is highly dependent on FF for a given image. System washout has a significant impact on the range and accuracy of displayed luminance, especially when the image generator estimates a static system CR.

[0038] In addition to losing scene content, the compression of luminance differences between adjacent grayscales near the system black level can significantly reduce the resolution of visual cues in the affected areas. Texture visibility, and to some extent runway marking visibility, is also reduced by system washout.

[0039] The FF-dependent TM operator according to the inventive concept can restore linearity to grayscales that would otherwise be unnaturally compressed or completely invisible. Because the image generator has instantaneous image data for the entire FOV and its output is adjusted to the luminance and chromaticity of a specific simulator by manual or automated alignment systems, the image generator can uniquely execute the FF-dependent TM operator to restore visible luminance previously lost due to system washout. For any particular image, it is possible to provide an in-image representation of the system black level, observer adaptive luminance, and luminance JND. Thus, nonlinear correction can be applied to the TM operator, effectively expanding the lost and distorted grayscale intensities until a visually linear output is achieved. While commandable bit depth may still be lost due to system washout, the intended resolution of darker luminance regions is largely restored by the relinearization provided by the FF-dependent TM.

[0040] Applying FF-dependent TM operators in real time has the potential to introduce visual artifacts with rapidly changing scene content. Also, significant changes in TM should be used over periods of time, potentially several seconds, that are visually imperceptible. As a result, the proposed method is best used as a means of providing deterministic, psychophysically-based recovery of lost or distorted dark grayscales across a truly continuous range of TOD.

[0041] An exemplary embodiment relates to a system-level approach to recovering grayscale lost due to system washout, where an alignment system identifies the continuous CR of the projector, and an image generator generates a continuous representation of the entire visual FOV using an FF-dependent model, allowing the image generator to provide a customized TM operator for the instantaneous image.

[0042] To generate a sufficiently large FOV and / or high-resolution image, a visual display system may use multiple projectors configured so that the projected images overlap. To ensure that the resulting image appears uniform, each component image must have corrections for spatial and radiometric non-uniformities. In applications with a relatively large FOV (e.g., between 40 degrees and 360 degrees wide), such as a flight simulator visual display system, these corrections are calculated by the alignment system (AS) and / or executed by the AS and / or image generator (IG) (e.g., an image processor).

[0043] While correction for static non-uniformities is well known and implemented, correction for dynamic non-uniformities, such as light contamination between adjacent pixels, has not been performed. As the scene displayed on the projection screen changes, the amount and location of light contamination also changes, resulting in ever-changing washout brightness that reduces scene contrast and the displayable brightness range. This is a performance-limiting factor for daytime, dusk, and dawn scenes, where the overall brightness is quite high.

[0044] Increasing the contrast ratio of a projection screen can significantly improve the instantaneous displayable luminance range. However, increasing the contrast ratio adversely affects brightness, and in many systems, this adversely affects lifecycle costs by shortening the life of the projector light source. Fortunately, in flight simulation applications, reducing the instantaneous luminance range generally has little impact on scene fidelity. This is due to the large distance between the pilot and objects outside the window. Even in clear weather (e.g., ceiling and visibility unlimited (CAVU)), atmospheric attenuation is noticeable in the scene, resulting in black objects appearing dark gray due to atmospheric scattering. This means that within these scenes, pixels are rarely, if ever, commanded to display black, and therefore luminance range is generally not realized for this content and is not a significant performance-limiting factor.

[0045] The reduction in scene contrast due to washout luminance is noticeable. Each scene uses numerous gray-to-gray contrasts, many of which are at or near the washout luminance level, and while this may shift the perceived location of the gray gradient in the image by a negligible amount (e.g., a fraction of a pixel), it may also result in a loss of fine detail or a premature loss of bounded targets due to a reduction in the gray-to-gray modulation transfer function (MTF). This is recognized in the industry, and contrast ratios in visual display systems are regulated parameters.

[0046] The inventive concept uses software and hardware to improve scene contrast. Contrast ratio is typically measured as the ratio of black to white and therefore is not affected by light pollution correction (LPC). However, direct measurement of contrast within a scene quantifies the gain and shows it to be comparable to at least a factor of two in contrast ratio. To maintain scene contrast gains, LPC must be recalculated for each scene, often at 120 Hz. LPC also works best with complete light pollution characterization to optimally predict the instantaneous light pollution of the scene on a pixel-by-pixel basis.

[0047] 1 illustrates a system 100 according to at least one example embodiment. The system 100 includes an image processing device (also referred to herein as a device or image generator or image processor) 104, a display system 108, and a database 112.

[0048] In at least one exemplary embodiment, device 104 corresponds to one or more of a personal computer (PC), a laptop, a tablet, a smartphone, a server, a collection of servers, etc. In some embodiments, device 104 may correspond to any suitable type of device that communicates with other devices similarly connected to a common type of communications network. As another specific, but non-limiting, example, device 104 may correspond to a user device, a client device, or a server that provides information resources, services, and / or applications to other hosts in system 100. In at least one exemplary embodiment, device 104 includes any suitable device that generates, receives, stores, and / or processes moving and / or still images.

[0049] The display system 108 includes one or more displays that display moving and / or still images received from the image processing device 104. The display system 108 can include any suitable type of display, such as a liquid crystal display (LCD), a light-emitting diode (LED) display, etc. The display system 108 can include one or more standalone devices or devices integrated as part of another device, such as a smartphone, laptop, or tablet. The display system 108 can be integrated with the device 104. In one non-limiting embodiment, the display system 108 is integrated with a flight simulator and includes one or more screens and one or more projectors that project images onto the one or more screens (see, e.g., FIG. 2). In this case, the one or more screens can include a full or partial dome or dome-like structure (e.g., made of an acrylic material) that is backlit by the one or more projectors. For example, a screen according to at least one embodiment can provide a horizontal field of view of 360 degrees or near 360 degrees and a vertical field of view of 135 degrees or near 135 degrees. In another example, the screen allows for a horizontal field of view of at or near 180 degrees and a vertical field of view of at or near 40 degrees. The exemplary embodiments are not limited to the above types of displays and / or fields of view described above, and any suitable display having any suitable field of view may be used in display system 108.

[0050] The database 112 may include any suitable type of memory device or collection of memory devices for storing data. Non-limiting examples of suitable memory devices that may be used include flash memory, random access memory (RAM), read-only memory (ROM), variations thereof, combinations thereof, etc. The database 112 may be remote from the device 104 and / or the display system 108, or may be local. In at least one embodiment, the database 112 stores a light pollution model and associated information. The light pollution model, described in more detail below with reference to the remaining figures, may include a data set having a light pollution value for each pixel displayed on the display of the display system 108. The pixel light pollution value may indicate the amount of light pollution experienced by that pixel (e.g., light pollution from ambient light and / or other pixels). The light pollution model may be used to generate a set of correction factors that may be applied when generating an image so that light pollution is reduced or eliminated from the image when displayed on the display. A correction factor may be generated for each pixel of the display so that light pollution for each pixel is reduced or eliminated. In at least one example, the light pollution model includes a calculated scattering coefficient for each pixel of the display, which can later be used to determine a correction factor for the pixel.

[0051] The device 104, the display system 108, and the database 112 may be communicatively coupled to one another by a communications network. The communications network may include a wired network and / or a wireless network enabling wired and / or wireless communication within the system 100. Examples of communications networks that may be used include an Internet Protocol (IP) network, an Ethernet network, an InfiniBand (IB) network, a Fibre Channel network, the Internet, a cellular communications network, a wireless communications network, combinations thereof (e.g., Fibre Channel over Ethernet), variations thereof, and the like. The communications network may enable wireless communication using one or more protocols from the 802.11 protocol suite, a Near Field Communication (NFC) protocol, a Bluetooth protocol, an LTE protocol, a 5G protocol, and the like. The device 104, the display system 108, and the database 112 may include one or more communications interfaces facilitating wired and / or wireless communication over the communications network. The device 104 and the display system 108 may be connected to each other by any suitable connection that carries video and / or audio signals, such as an HDMI connection, a DisplayPort connection, an RS232 connection, a BNC connection, an RJ45 connection, or the like.

[0052] Although the device 104, the display system 108, and the database 112 are shown as separate entities communicating over a communications network, it should be understood that these elements may be incorporated into a single device (e.g., a server, a personal computer, etc.).

[0053] The processing circuitry 116 may include appropriate software, hardware, or a combination thereof to process images from the source device 104 and perform other types of computing tasks. The processing circuitry 116 may perform various image processing operations and algorithms described herein. The memory 120 may include executable instructions, and the processing circuitry 116 may execute the instructions on the memory 120. Thus, the processing circuitry 116 may include a microprocessor, microcontroller, or the like to execute the instructions on the memory 120. The memory 120 may correspond to any appropriate type of memory device or collection of memory devices that stores instructions. Non-limiting examples of suitable memory devices that may be used include flash memory, random access memory (RAM), read-only memory (ROM), variations thereof, combinations thereof, and the like. In some embodiments, the memory 120 and the processing circuitry 116 may be integrated into a common device (e.g., a microprocessor may include integrated memory). Additionally or alternatively, the processing circuitry 116 may include hardware such as an application-specific integrated circuit (ASIC). Other non-limiting examples of processing circuitry 116 include an integrated circuit (IC) chip, a central processing unit (CPU), a graphics processing unit (GPU), a field programmable gate array (FPGA), a digital signal processor (DSP), a collection of logic gates or transistors, resistors, capacitors, inductors, diodes, etc. Some or all of processing circuitry 116 may be provided on a printed circuit board (PCB) or a collection of PCBs. It should be understood that any suitable type of electrical component or collection of electrical components may be suitable for inclusion in processing circuitry 116. Processing circuitry 116 may send and / or receive signals to and from other elements of system 100 to control various operations of system 100.

[0054] Input device(s) 124 include appropriate hardware and / or software that enable input to system 100 (e.g., user input) via device 104. Input device(s) 124 may include a keyboard, a mouse, a touch-sensitive pad, a touch-sensitive button, a touch-sensitive portion of a display, mechanical buttons, switches, and / or other control elements that provide user input to system 100 to enable user control over certain functions of system 100.

[0055] Output device(s) 128 may include appropriate hardware and / or software to generate visual, audio, and / or tactile feedback for a user or other party based on one or more inputs from processing circuit 116. In at least one exemplary embodiment, output device 128 includes one or more displays that display an output image and / or one or more characteristics of the input image after processing of the input image by device 104. The input image may be based on a source image received from source device 104 over a communications network. The display(s) may include any suitable type of display, such as a liquid crystal display (LCD), a light-emitting diode (LED) display, etc. Output device 128 may be a standalone device or a device integrated as part of another device, such as a smartphone, laptop, tablet, etc.

[0056] Although the input device 124 and the output device 128 are shown as part of the image processing device 104, the input device 124 and / or the output device 128 may be embodied separately from the device 104 according to the design preferences of the system 100.

[0057] Additionally, it should be understood that device 104, display system 108, and / or database 112 may include other processing devices, storage devices, and / or communication interfaces typically associated with computing tasks, such as transmitting and receiving data via wired and / or wireless connections.

[0058] FIG. 2 illustrates various exemplary implementations of the display system 108 of FIG. 1. More specifically, FIG. 2 illustrates two implementations of the display system 108 that may be useful in a flight simulator. Accordingly, the display system 108 in both implementations includes one or more projectors 200a, 200b, and 200c and a display or screen 204 having a dome structure. During operation, the image processing unit 104 controls the projectors 200a through 200c to display moving and / or still images on the rear side of the screen 204 for viewing by users seated in one or more seats 208 in front of the screen 204 within the dome. In the flight simulator example, the seat 208 may be a pilot's seat, and although not explicitly shown, a control panel and other flight instruments associated with the cockpit may be located within the dome proximate to the seat 208 to enable the user seated in the seat 208 to control the simulated flight as depicted on the screen 204.

[0059] 2 illustrates one or more image sensors (e.g., cameras) 212 that may be positioned in a central region of screen 204. As described in more detail below, image sensor 212 may be used to capture one or more calibration images displayed on screen 204 for purposes of building a light pollution model that may be stored in database 112. Sensor 212 may operate at a different resolution than one or more of projectors 200a, 200b, and 200c. For example, in some embodiments, sensor 212 has a lower resolution than at least one of projectors 200a, 200b, and 200c.

[0060] FIG. 3 illustrates exemplary calibration image patterns 300 and 304 that may be displayed on screen 204 by projectors 200a-200c. As can be seen, pattern 300 includes alternating black and white stripes of equal width extending horizontally across screen 204, and pattern 304 includes alternating black and white stripes of equal width extending vertically across screen 204. Both patterns 300 and 304 include black and white images with repeating patterns. It should be understood that patterns other than those illustrated in FIG. 3 can be used as calibration images for constructing a light contamination model (e.g., checkerboard, dotted, diagonal, etc.). Additionally, the calibration image patterns are not limited to black and white patterns and may use any suitable color combination.

[0061] FIG. 4 illustrates a method 400 according to at least one example embodiment. While a general order of steps of method 400 is illustrated in FIG. 4, method 400 may include more or fewer steps, or may arrange the steps in a different order than that illustrated in FIG. 4. Method 400 may be implemented as a set of computer-executable instructions encoded or stored in memory 120 and executed by processing circuitry 116. In another embodiment, one or more of the operations of method 400 are performed by processing circuitry 116 in the form of an ASIC. Method 400 may be described with reference to the systems, components, assemblies, devices, user interfaces, environments, software, etc., described in connection with FIGS. 1-3 . In general, the method of FIG. 4 illustrates calibration operations performed to generate a light pollution model in accordance with the concepts of the present invention.

[0062] In general, light pollution models consider light pollution as unwanted light from a given pixel or group of pixels distributed among other pixels. In at least one embodiment, such unwanted light is assumed to be substantially uniformly distributed across all other pixels in the field of view (e.g., the field of view of a projector). As noted in the preceding description of FIG. 1, the fill factor of a displayed image may be determined by converting the pixel values of the image to grayscale, summing the grayscale values from all pixels of the image, and dividing that sum by the sum of the grayscale values from all pixels of a completely white image, as instructed by the image processor 104. The light pollution model may be used to generate a correction factor (e.g., a tone map operator or brightness adjustment) that can be applied to pixels of a scene during the image generation process, where the correction factor varies based on the expected fill factor of that scene. As part of the correction process, the fill factor of a scene (e.g., frame) may be determined by converting the pixels of the scene to grayscale and dividing the sum of the grayscale by the grayscale of a completely white image displayed on the same display that will ultimately be used to display the scene. A correction factor for the pixels of the scene may then be obtained based on the calculated fill factor of the scene. As mentioned above and below, the correction factors can correspond to tone map operators that adjust the luminance of pixels to eliminate or reduce the light contamination associated with each pixel, thereby increasing the system contrast ratio without having to sequentially increase the contrast ratio of the display or projector.

[0063] Act 404 includes displaying the calibration image on a display, for example, on screen 204. As mentioned above, the calibration image may be one of images 300 or 304 of FIG. 3 displayed simultaneously on screen 204 by projectors 200a-200c. As can be appreciated, each projector 200a-200c can correspond to a different "display," even when the projectors are working together to display a single adjacent scene.

[0064] Operation 408 includes capturing the calibration image displayed on the display using one or more image sensors 212. For example, the one or more image sensors 212 may be implemented with a camera ball having multiple integrated cameras and / or multiple individual cameras positioned on the back of the screen 204. In one non-limiting example, the number of cameras used for the 360-degree screen 204 may equal eight, with each camera having the same or similar field of view (e.g., a 120-degree field of view) aimed at a respective section of the screen 204. However, the number of cameras and their respective fields of view may vary depending on design preference. The cameras may be positioned at a design eyepoint, which may be a position corresponding to the eye height of a user seated in the seat 208. In some embodiments, the one or more cameras are aligned with the light beam at the design eyepoint. The cameras may capture the calibration image under ambient light conditions typically present when the screen 204 is used for flight simulation or other purposes. In another embodiment, the cameras capture the calibration image under dark conditions.

[0065] Operation 412 includes determining light contamination of zones of the calibration image based on the captured calibration image. For example, the image processing device 104 determines scattering coefficients of zones of the captured calibration image, where each zone of the captured calibration image corresponds to a single pixel or a group of adjacent pixels of the screen 204. In this regard, the image processing device 104 may collect information about the brightest white and the brightest black in the calibration image displayed on the screen 204. As can be appreciated, the image processing device 104 may perform a mapping of pixels in the captured calibration image to pixels of the screen 204 to accurately determine the scattering coefficient of the pixels of the screen 204. In at least one embodiment, the scattering coefficient of each pixel of the screen 204 may be determined using information about the known luminance of the screen pixel (e.g., the luminance value of the pixel as commanded by the image processing device 104 of the projector 200) and the captured luminance value of the same pixel captured by one or more image sensors 212 (the difference between the known luminance value of the pixel and the captured luminance value may indicate the scattering coefficient of the pixel). In some examples, the following pseudocode is used to determine pixel scattering coefficients using two calibration images: a first image that is 50% white and 50% black, such as the example image patterns 300 or 304 captured by image sensor 212; and a second image that is the inverse of the first image (i.e., all white areas in the first image are black in the second image, and all black areas in the first image are white in the second image). The darkest and whitest luminance values of each pixel are measured in each captured image. Such luminance measurements may be linear in nature (e.g., ranging from 0.0 to 1.0), and the resulting scattering coefficient for each pixel is unitless. The pseudocode for calculating the scattering coefficients in the two captured images is as follows: For each display d For each pixel i scattering_coefficient[d][i]=2*black[d][i] / (white[d][i]-black[d][i])

[0066] Because scattering coefficients typically vary slowly throughout a display system, they can be interpolated from lower resolution measurements (e.g., if a single calibration image is used instead of two images as in the example above, the white luminance measurements of black areas of the single image are estimated or interpolated from the white luminance measurements of white areas of the image near the black areas, and vice versa, e.g., black pixels in the image are assigned the same white luminance value as the white pixels nearest to them (and vice versa for white pixels assigned black luminance values)). Correction factors can be generated using a single measurement of the scattering coefficient determination.

[0067] Determining the scattering coefficient for each pixel of screen 204 based on a captured calibration image is a useful metric of light contamination because the scattering coefficient is substantially independent of the color displayed for that pixel in the scene. Therefore, calculating the scattering coefficient using a relatively simple black-and-white calibration image (such as image 300 or image 304) is an efficient way to gather information about light contamination between adjacent pixels of a color scene displayed on screen 204. As described in more detail below, the scattering coefficient can be used to construct a light contamination model that is used to predict and correct the instantaneous light contamination of the scene for each pixel (or each non-pixel zone) of screen 204.

[0068] Operation 416 includes generating a light pollution model based on the determined light pollution from operation 412. For example, operation 416 generates the light pollution model based on the calculated scattering coefficients. In at least one embodiment, as described in more detail below, the light pollution model can be used to generate correction factors (e.g., brightness adjustments) for each zone (e.g., each pixel) of a displayed image. In at least one example, the light pollution model includes estimates of light pollution over a range of possible fill factors. Stated another way, the fill factor of a display changes as the displayed image changes, and thus the light pollution model includes a light pollution value for each possible fill factor value. In an example where the fill factor ranges from 0.00 to 1.00, the light pollution model can include 101 light pollution values, one value for each possible fill factor of 0.00, 0.01, 0.02, etc., up to 1.00. In at least one embodiment, each light contamination value is a vector "pollution.rgb" defined for each pixel i of display d as follows: pollution.rgb=FillFactor.rgb*scattering_coefficient[d][i]. The FillFactor.rgb of a scene to be displayed on screen 204 by projector 200 may be calculated in real time according to the following description in FIG. 5.

[0069] As mentioned above, each zone in the calibration image can correspond to a pixel on the screen 204, and therefore the light contamination model can be used to control the brightness adjustment of that pixel, thereby allowing the system to predict and correct for light contamination on a pixel-by-pixel basis. The tone map operator can be static for each pixel or can be variable according to specific conditions.

[0070] FIG. 5 illustrates a method 500 according to at least one example embodiment. While a general order of steps of method 500 is illustrated in FIG. 5, method 500 may include more or fewer steps, or may arrange the steps in an order different from that illustrated in FIG. 5. Method 500 may be implemented as a set of computer-executable instructions encoded or stored in memory 120 and executed by processing circuitry 116. In another embodiment, one or more of the operations of method 500 are performed by processing circuitry 116 in the form of an ASIC. Method 500 may be described with reference to the systems, components, assemblies, devices, user interfaces, environments, software, etc., described in connection with FIGS. 1-4. In general, the method of FIG. 5 illustrates operations performed to generate a scene including per-pixel luminance correction.

[0071] Operation 504 includes generating a light pollution model based on at least one calibration operation. Operation 504 may therefore correspond to method 400 of FIG. 4 , which illustrates details of at least one calibration operation. As described above and below, the light pollution model may include light pollution values calculated from scattering coefficients of pixels p of display d, and may be used to adjust, in real time, the initial luminance of pixels in a scene to a final luminance to reduce light pollution, such as image washout, thereby increasing the effective contrast ratio of the scene when displayed on screen 204. (Recall that screen 204 may include multiple displays, each corresponding to an area of screen 204 illuminated by a respective projector 200.) It should be understood that operation 504 may be skipped or omitted from method 500, for example, if the light pollution model has already been generated (e.g., during a previous iteration of method 500 or during a separate instance of method 400). In general, the light pollution model can be updated (regenerated) when one or more components that may affect brightness are changed in the system (e.g., after projector maintenance such as replacing a light bulb, replacing a projector, relocating a projector, etc.).

[0072] Operation 508 includes the image processor 104 receiving a portion of a scene having an initial brightness, for example, as commanded by a video signal or still image signal having a scene. In the case of a video signal (e.g., a 60 Hz or 120 Hz video signal), the scene may correspond to frames of the video signal. In this case, operations 508 through 516 may be performed iteratively in real time, such that each frame of the video signal rendered on the display system 108 is analyzed and the brightness is adjusted according to a light pollution model. In one embodiment, the scene corresponds to a still image rendered on the display system 108. The portion of the scene may correspond to a single pixel or a group of pixels when the scene is displayed on the display system 108. For example, the portion of the scene may be mapped to a single pixel on the screen 204 or to a group of adjacent pixels on the screen 204. In some cases, operation 508 may include converting image information of the portion of the scene to an initial brightness value and / or determining the initial brightness value of the portion of the scene in an appropriate manner.

[0073] A portion of a scene may have one or more luminance values, such as a linear luminance value ranging from 0.0 to 1.0 (which may have corresponding RGB luminance values) as described herein. For example, if a portion of a scene corresponds to a single pixel, the initial luminance of the pixel may correspond to a preset luminance value of the pixel determined by image processor 104 for rendering the scene on display system 108. In another example, if a portion of a scene corresponds to a group of pixels, the initial luminance may be an average luminance value of the preset luminance values of the pixels, a median luminance value of the preset luminance values of the pixels, and / or another suitable luminance value that describes one or more preset luminance values of the pixels.

[0074] Act 512 includes adjusting the initial luminance of the portion of the scene to a final luminance based on the light pollution model generated in act 504, where the luminance adjustment can compensate for per-pixel light pollution that would otherwise occur when the scene is rendered on display system 108 without the luminance adjustment. As mentioned above, the light pollution model may be associated with a display, which in some examples corresponds to projector 200. In a system with multiple displays or projectors, the light pollution model may be determined separately for each display or projector, or may be determined for one display or projector and applied to the remaining displays or projectors.

[0075] Operation 516 includes rendering the scene to a display with the portion of the scene having the final brightness. In at least one exemplary embodiment, multiple pixels or all pixels in the scene may have their brightness adjusted in operation 512 such that the entire displayed scene reduces or eliminates light contamination compared to the initial signal.

[0076] Exemplary embodiments relating to operations 512 and 516 are described in more detail below, beginning with a description of the various equations used to adjust the initial luminance values to final luminance values. If all displays (i.e., projector 200) have identical or nearly identical display characteristics (e.g., number of pixels, FOV, color range, etc.), as is typically the case, a peak (or maximum) display luminance of 1.0 can be assumed for each display. X.rgb represents a pixel's color vector, which is a linearized rgb luminance value ranging from the minimum to the maximum possible value, such as 0.0 to 1.0. The following description avoids the use of non-linear sRGB-encoded integer values (e.g., in the ranges of 0 to 255 or 0 to 1023) because the use of non-linearly encoded data is not supported by the following equations. Using linear 0 to 1 values is common in graphics rendering documentation and GPU programming because the conversion from linear 0 to 1 values to display-specific encoding (e.g., 0 to 255) is not typically part of a GPU programming interface. However, it should be understood that a linear luminance rgb value can have a corresponding non-linear luminance sRGB value.

[0077] The luminance value averaging may be performed in one or more GPUs of processing circuit 116, and the average may be calculated at the frame rate of the video or image being displayed. The real-time fill factor calculation for a scene (FillFactor.rgb) may be performed according to the following pseudocode, where the "+=" notation is equivalent to sigma notation indicating an addition operation: For each display d, for each pixel i, average_fill_factor[d].rgb+=input_pixel[d][i].rgb / #display_pixels[d] FillFactorSum.rgb+=peak_display_luminance[d]*average_fill_factor[d].rgb FillFactorMax.rgb+=peak_display_luminance[d] FillFactor.rgb=FillFactorSum.rgb / FillFactorMax.rgb

[0078] In the above formula, --input_pixel[d][i].rgb is the initial luminance of pixel i in display d when it is first commanded to be displayed on screen 204; --#display_pixels[d] is the total number of pixels on display d, --FillFactorSum.rgb is a measure of the overall fill factor of all displays d, --FillFactorMax.rgb is the maximum fill factor value for your system (assumed to be 1.0 in this example). As can be seen, FillFactor.rgb is a normalized real-time fill factor ranging from 0.0 to 1.0. The real-time FillFactor.rgb is then used to apply a brightness correction on a pixel-by-pixel (or zone-by-zone) basis.

[0079] The pixel-by-pixel correction can be performed in the GPU at frame rate according to the pseudocode below, which uses the pollution value pollution.rgb from the light pollution model from FIG. For each display d, for each pixel i in the scene to be displayed, the light contamination is determined as follows: pollution.rgb=FillFactor.rgb*scattering_coefficient[d][i] The final luminance value of the pixel can be calculated as follows: pixel.rgb=input_pixel[d][i].rgb*(1.rgb+pollution.rgb)-pollution.rgb

[0080] As mentioned above, pollution.rgb is a value included in the light pollution model that indicates the amount of light pollution for pixel i, and is calculated by multiplying the real-time fill factor value FillFactor.rgb by the scattering coefficient for pixel i. Then, with 1.rgb as the peak intensity color vector of (1,1,1), the final intensity value for pixel i, pixel.rgb, is calculated by multiplying the initial intensity value input_pixel[d][i].rgb by (1.rgb + pollution.rgb) and subtracting pollution.rgb from the result.

[0081] In some examples, the system implements tone mapping to prevent or mitigate loss of detail in dark areas according to the following pseudocode: tone_map.rgb=min(pixel.rgb-pollution.rgb, 0.rgb), where 0.rgb is the minimum luminance color vector at (0,0,0) and tone_map.rgb is an intermediate value used as input in the formula below. output_pixel[d][i].rgb=pixel.rgb+tone_map.rgb*tone_map.rgb / (4*pollution.rgb) Here, output.pixel[d][i].rgb is a tone mapping operation that restores contrast to pixels whose final luminance value is near or below 0 by mapping that value to a higher value. In other words, the tone mapping operation output.pixel[d][i].rgb may be applied to those pixels that have a final luminance value pixel.rgb that is less than or approximately 0.

[0082] In at least one exemplary embodiment, instead of or in addition to the brightness adjustment described in the equations above, operation 512 includes selecting a correction factor based on the brightness threshold from operation 508 and the initial brightness, which may correspond to weights applied to RGB brightness values or other suitable means of adjusting brightness. Operation 512 may then further include applying the selected correction factor to the portion of the scene to adjust the initial brightness of the portion to a final brightness of the portion. In general, the brightness threshold may be a design parameter set based on empirical evidence and / or preference and may be stored in a memory or database and accessible by the image processing device. For example, the brightness threshold may be based on or equal to a leakage level of projector 200 projecting the portion of the scene onto screen 204. The brightness threshold may be the same or different for multiple or all pixels of screen 204. Selecting the correction factor as part of operation 512 may include selecting a first correction factor that, when the initial brightness exceeds the brightness threshold, reduces the initial brightness of the portion of the scene to the final brightness by a first amount substantially equal to the amount of light contamination predicted for the portion of the scene by a light contamination model. For example, the light contamination model may predict that a luminance value of a first pixel that exceeds the luminance threshold will induce a known amount of light contamination at one or more neighboring pixels based on the scattering coefficients calculated in method 400. A first correction factor may then be selected to reduce the initial luminance of the first pixel that exceeds the luminance threshold by an amount that offsets or substantially reduces the light contamination induced by the first pixel on the neighboring pixels.

[0083] Selecting a correction factor as part of operation 512 may include selecting a second correction factor that reduces the initial luminance by a second amount that is less than the amount of light contamination predicted for the portion of the scene by the light contamination model when the initial luminance is equal to or less than the luminance threshold. Thus, the second amount may be less than the first amount of the first correction factor. In at least one embodiment, the second amount decreases as the initial luminance falls further below the luminance threshold. The second amount (the amount of luminance reduction from the initial luminance) may be inversely proportional to the difference between the initial luminance and the luminance threshold. That is, the amount of luminance reduction relative to the second amount decreases as the difference between the initial luminance and the luminance threshold increases. It should be understood that the second amount may be equal to zero (i.e., final luminance = initial luminance) when the initial luminance is at the minimum possible luminance achievable by the display system 108. By using a second correction factor that gradually decreases the amount of correction as the difference between the brightness threshold and the initial brightness increases, the system can make a brightness correction that is smaller than the first correction factor to avoid excessive reduction in the initial brightness of a pixel as it approaches the minimum possible brightness of the display system 108 (where the minimum possible brightness is based on the capabilities and noise of the projector 200).

[0084] In at least one embodiment, operation 512 can take into account an additional luminance threshold that is higher than the aforementioned luminance threshold. If it is determined that the initial luminance of the pixel is equal to or greater than the additional luminance threshold, a third correction factor can be selected to adjust the initial luminance of the pixel. In this case, the greater the difference between the initial luminance and the additional luminance threshold, the smaller the amount of luminance reduction caused by the third correction factor (e.g., when the initial luminance of the pixel is at the maximum possible luminance of the display system 108, the smaller the amount of luminance reduction until the correction is zero). The additional luminance threshold can also be a design parameter set based on empirical evidence and / or preference.

[0085] Although not explicitly shown, in at least one embodiment, method 500 determines whether the intended color of a pixel in a scene changes by more than a color difference threshold upon application of the first correction factor or the second correction factor. If so, the first correction factor or the second correction factor can be modified (e.g., the luminance is reduced) so that the change in color of the pixel is less than the color difference threshold. If not, the first correction factor or the second correction factor can be applied to the pixel without further modification.

[0086] In at least one embodiment, the correction factors described above may vary based on the time of day setting of the flight simulation. For example, the tone map operator for each pixel may be adjusted globally up or down depending on the time of day or ambient lighting conditions being simulated (e.g., dusk, dawn, sunny daytime, night, overcast, rain, snow, etc.). In this manner, the image processing systems, methods, and apparatuses disclosed herein can adjust the initial brightness for any time of day in a scene. In contrast, some prior art methods for adjusting brightness work well for one time of day (e.g., daytime scenes) but perform poorly at other times (e.g., dusk, dawn, and / or night).

[0087] It should be appreciated that operation 512 may be performed on a global basis for display system 108. That is, a correction factor (e.g., a tone map operator) applied to a pixel of a scene may be modified based on the instantaneous light pollution at that pixel as predicted by a light pollution model in response to the expected luminance of all pixels in the scene.

[0088] Although the correction factors are described as being selected, it should be understood that the correction factors referred to herein may additionally or alternatively be calculated or determined using a suitable formula or formulas. Furthermore, while the brightness adjustments and correction factors have been described with reference to projector-style displays, the same concepts may be applied to other displays, such as LED, OLED, LCD, etc.

[0089] As can be understood from the foregoing description, exemplary embodiments improve the contrast ratio perception of an observer (e.g., improved contrast with the above-described adjustments implemented by software). Additionally, exemplary embodiments can reduce the intensity of light emitted from a projector, thereby extending the life of the projector system (e.g., extended bulb life). Exemplary embodiments can also enable lower-quality projectors to produce high-quality images on a screen.

[0090] In view of the present disclosure, an exemplary embodiment relates to a method that includes displaying a calibration image on a display, capturing the calibration image displayed on the display using one or more image sensors, and determining light contamination of zones of the calibration image displayed on the display. In one embodiment, equal-sized black / white latitudinal stripes propagate from pole to pole while one or more industrial cameras collect white / black linear peak signal levels per pixel. The operation is repeated for unequal-sized black / white latitudinal stripes. The method may include generating a correction factor for each zone of the calibration image based on the determined light contamination and generating a light contamination model based on at least one calibration operation. In at least one embodiment, a camera pixel to pixel mapping is used to convert data from the camera to a display space. Furthermore, a per-pixel light contamination model is derived from the data and recorded for later use. The method may further include determining an initial luminance of a portion of a scene to be displayed on the display and adjusting the initial luminance of the portion of the scene based on the light contamination model to compensate for light contamination when the scene is displayed on the display. In one embodiment, an initial image to be displayed is acquired. For the initial image, a light contamination correction is calculated for each pixel's color value (red, green, blue) using a light contamination model. The light contamination correction is smoothly attenuated toward zero for very low color values (e.g., "very low color values" may contribute less than 1% of the peak white intensity), which can prevent or mitigate loss of detail in very dark areas of the image by maintaining the correction from subtracting large magnitude contamination from lower magnitude color values. Additionally, the light contamination correction is smoothly attenuated toward zero when the corrected color value deviates from the initial color value by a color difference threshold (e.g., the "color difference threshold" may be a value of 1.0 in CIELAB delta E color coordinates, e.g., https: / / en.wikipedia.org / wiki / Color_difference), which can ensure that the image color does not change noticeably.The method may include finally rendering the scene to a display with the portion of the scene having a final luminance. In one embodiment, each image generation channel applies light contamination correction to its output frame buffer on a pixel-by-pixel, color-by-color basis in a post-processing step immediately prior to transmission to the display device. The above method is repeated for each subsequent image at the frame rate of the display system.

[0091] It is understood that the inventive concept encompasses any embodiment in combination with any one or more other embodiments, any one or more features disclosed herein, any one or more features substantially disclosed herein, any one or more features substantially disclosed herein in combination with any one or more other features substantially disclosed herein, any one of the aspects / features / embodiments in combination with any one or more other aspects / features / embodiments, and the use of any one or more embodiments or features disclosed herein. It is understood that any feature described herein may be claimed in combination with any other feature described herein, regardless of whether the features are from the same described embodiment. A system-on-chip (SoC) including any one or more of the above aspects.

[0092] An exemplary embodiment may be configured according to the following. (1) A method for reducing light contamination in a displayed image, comprising: receiving a portion of a scene to be displayed on a display at an initial brightness; adjusting an initial luminance of a portion of the scene to a final luminance based on a light pollution model associated with the display; Rendering the scene to a display with the portion of the scene having a final luminance; A method comprising: (2) The method of (1), further comprising generating a light pollution model based on at least one calibration operation. (3) at least one calibration operation; Displaying the calibration image on a display; capturing, with one or more image sensors, a calibration image displayed on a display; determining light contamination of one or more zones of the calibration image based on the captured calibration image; The method according to one or more of (1) to (2), comprising: (4) A method according to one or more of (1) to (3), wherein the calibration image comprises a black and white image having a repeating pattern. (5) A method according to one or more of (1) to (4), wherein determining light contamination of one or more zones of the calibration image includes determining a scattering coefficient of one or more zones of the captured calibration image. (6) A method according to one or more of (1) to (5), wherein each zone of the captured calibration image corresponds to a single pixel of the display. (7) The method according to one or more of (1) to (6), further comprising generating a correction factor for each zone of the calibration image based on the determined scattering coefficient. (8) A method according to one or more of (1) to (7), wherein the correction factor includes a brightness-adjusting tone map operator. (9) adjusting the initial luminance of a portion of the scene to a final luminance based on a light pollution model; selecting a correction factor based on the luminance threshold and the initial luminance; applying a selected correction factor to portions of the scene to adjust the initial luminance to a final luminance; The method according to one or more of (1) to (8), comprising: (10) A method according to one or more of (1) to (9), wherein selecting a correction factor includes selecting a first correction factor that reduces the initial luminance of the portion of the scene by a first amount to the final luminance when the initial luminance is above a luminance threshold, the first amount being substantially equal to an amount of light pollution predicted for the portion of the scene by the light pollution model. (11) A method according to one or more of (1) to (10), wherein selecting a correction factor includes selecting a second correction factor that reduces the initial luminance by a second amount less than the amount of light pollution predicted for the portion of the scene by the light pollution model when the initial luminance is equal to or less than a luminance threshold, and the second correction factor is different from the first correction factor. (12) The method according to one or more of (1) to (11), wherein the second amount decreases more as the initial brightness falls further below the brightness threshold. (13) A method according to one or more of (1) to (12), wherein the portion of the scene corresponds to a single pixel. (14) An apparatus for reducing light contamination in a displayed image, comprising: a processing circuit, the processing circuit comprising: receiving the portion of the scene to be displayed on the display at an initial luminance; adjusting an initial luminance of a portion of the scene to a final luminance based on a light pollution model associated with the display; An apparatus that renders a scene to a display with portions of the scene having a final luminance. (15) A processing circuit, selecting a correction factor based on the luminance threshold and the initial luminance; applying a selected correction factor to portions of the scene to adjust the initial luminance to a final luminance; 15. The apparatus of claim 14, wherein the initial luminance of the portion of the scene is adjusted to a final luminance based on a light pollution model. (16) The device described in one or more of (14) to (15), wherein selecting a correction factor includes selecting a first correction factor that reduces the initial brightness by a first amount less than the amount of light pollution predicted for the portion of the scene by the light pollution model when the initial brightness is less than or equal to a brightness threshold. (17) The device of one or more of (14) to (16), wherein the first amount decreases more as the initial brightness falls further below the brightness threshold. (18) The device described in one or more of (14) to (17), wherein selecting the correction factor includes selecting a second correction factor that reduces the initial brightness of the portion of the scene by a second amount to the final brightness when the initial brightness is above a brightness threshold, the second amount being substantially equal to the amount of light pollution predicted for the portion of the scene by the light pollution model. (19) The device according to one or more of (14) to (18), wherein the portion of the scene corresponds to a single pixel of the display. (20) A system for reducing light contamination in a displayed image, comprising: a processing circuit, the processing circuit comprising: determining an initial luminance of a pixel within each of a plurality of frames of a video signal to be displayed on a display; For each frame, adjust the initial luminance of pixels in each frame to a final luminance based on a light pollution model that compensates for light pollution that occurs when each frame is displayed on the display; A system that renders each frame to a display with pixels having a final luminance. (21) A method of calibrating a display system, comprising: Displaying the calibration image on a display; capturing, with one or more image sensors, a calibration image displayed on a display; determining light contamination of a zone of the calibration image based on the captured calibration image; generating a light pollution model based on the determined light pollution for the zone; A method comprising: (22) The method of (21), wherein the calibration image comprises a black and white image having a repeating pattern. (23) A method according to one or more of (21) to (22), wherein determining light contamination of a zone of the calibration image includes determining a scattering coefficient of the zone of the captured calibration image. (24) A method according to one or more of (21) to (23), wherein each zone of the captured calibration image corresponds to a single pixel of the display. (25) A system comprising: The screen and one or more projectors for projecting images onto a screen; one or more image sensors; an image processing circuit; The image processing circuit includes: Rendering the calibration image onto a screen using one or more projectors; controlling one or more image sensors to capture a calibration image displayed on a screen; determining light contamination of a zone of the calibration image based on the captured calibration image; generating a light pollution model based on the determined light pollution of the zone; system. (26) The system of (25), wherein the calibration image comprises a black and white image having a repeating pattern. (27) A system described in one or more of (25) to (26), wherein determining light contamination of a zone of the calibration image includes determining a scattering coefficient of the zone of the captured calibration image. (28) A system according to one or more of (25) to (27), wherein each zone of the captured calibration image corresponds to a single pixel of the screen.

Claims

1. 1. A method for reducing light contamination in a displayed image, comprising: receiving a portion of a scene to be displayed on a display at an initial brightness; adjusting the initial luminance of the portion of the scene to a final luminance based on a light pollution model associated with the display; rendering the scene on the display with the portion of the scene having the final luminance; A method comprising:

2. The method of claim 1 , further comprising generating the light pollution model based on at least one calibration operation.

3. The at least one calibration operation comprises: displaying a calibration image on said display; capturing, with one or more image sensors, the calibration image displayed on the display; determining light contamination of one or more zones of the calibration image based on the captured calibration image; The method of claim 2 , comprising:

4. The method of claim 3 , wherein the calibration image comprises a black and white image having a repeating pattern.

5. The method of claim 3 , wherein determining the light contamination of the one or more zones of the calibration image comprises determining a scattering coefficient of the one or more zones of the captured calibration image.

6. The method of claim 4 , wherein each zone of the captured calibration image corresponds to a single pixel of the display.

7. The method of claim 5 , further comprising generating a correction factor for each zone of the calibration image based on the determined scattering coefficients.

8. The method of claim 7 , wherein the correction factor comprises a luminance-adjusting tone map operator.

9. adjusting the initial luminance of the portion of the scene to the final luminance based on the light pollution model; selecting a correction factor based on a luminance threshold and the initial luminance; applying the selected correction factor to the portion of the scene to adjust the initial luminance to the final luminance; 6. The method of claim 1, comprising:

10. 10. The method of claim 9, wherein selecting the correction factor comprises selecting a first correction factor that reduces the initial luminance of the portion of the scene by a first amount to the final luminance if the initial luminance is above the luminance threshold, the first amount being substantially equal to an amount of light pollution predicted for the portion of the scene by the light pollution model.

11. 11. The method of claim 10, wherein selecting the correction factor includes selecting a second correction factor that reduces the initial luminance by a second amount less than the amount of light contamination predicted for the portion of the scene by the light pollution model when the initial luminance is less than or equal to the luminance threshold, the second correction factor being different from the first correction factor.

12. The method of claim 11 , wherein the second amount decreases more as the initial brightness falls further below the brightness threshold.

13. The method of claim 1 , wherein the portion of the scene corresponds to a single pixel.

14. 1. An apparatus for reducing light contamination in a displayed image, comprising: a processing circuit, the processing circuit comprising: receiving the portion of the scene to be displayed on the display at an initial luminance; adjusting the initial luminance of the portion of the scene to a final luminance based on a light pollution model associated with the display; An apparatus that renders the scene on the display with the portion of the scene having the final luminance.

15. the processing circuitry selecting a correction factor based on a luminance threshold and the initial luminance; applying the selected correction factor to the portion of the scene to adjust the initial luminance to the final luminance; 15. The apparatus of claim 14, wherein the initial luminance of the portion of the scene is adjusted to the final luminance based on the light pollution model by:

16. 16. The apparatus of claim 15, wherein selecting the correction factor comprises selecting a first correction factor that reduces the initial luminance by a first amount less than an amount of light contamination predicted for the portion of the scene by the light contamination model if the initial luminance is less than or equal to the luminance threshold.

17. 17. The apparatus of claim 16, wherein the first amount decreases more as the initial brightness falls further below the brightness threshold.

18. 17. The apparatus of claim 16, wherein selecting the correction factor comprises selecting a second correction factor that reduces the initial luminance of the portion of the scene by a second amount to the final luminance if the initial luminance is above the luminance threshold, the second amount being substantially equal to an amount of light contamination predicted for the portion of the scene by the light pollution model.

19. 19. Apparatus according to any one of claims 14 to 18, wherein the portion of the scene corresponds to a single pixel of the display.

20. 1. A system for reducing light contamination in a displayed image, comprising: a processing circuit, the processing circuit comprising: determining an initial luminance of a pixel within each of a plurality of frames of a video signal to be displayed on a display; adjusting, for each frame, the initial luminance of the pixels in each frame to a final luminance based on a light pollution model that compensates for light pollution that occurs when each frame is displayed on the display; The system renders each frame on the display using the pixels with the final luminance.

Images