Patent Description:
Some HDR content is "created" using content creation tools, and not captured by recording devices such as cameras. Examples of such creation scenarios include video games, and animated movies and visual effects ("VFX") in movies. HDR content may also by "auto-created" algorithmically. This type of HDR content is neither recorded nor manually created with content creation tools.

In many cases, HDR content is created and/or edited using SDR display devices. Video games, for example, have been producing HDR images in real-time for many years, with the first video game to utilize HDR lighting techniques as early as <NUM>. To view a game's real-time HDR images on an SDR display, the HDR values in these images first must be processed into a much smaller range of brightness and color values. Such processing is typically referred to as tone mapping. HDR displays are capable of native display of HDR content without the need for tone mapping, which naturally results better visual quality.

Until around <NUM>, however, all consumer display devices were SDR and incapable of natively displaying HDR content. Thus, the vast majority of consumers still have only SDR display devices. Moreover, the current low penetration of HDR displays (especially smaller sized displays that can easily fit onto a desk) means that even most video game content creators still use SDR display devices, and indeed, many content creation tools still do not support HDR display devices. In other words, most video game content is still mastered in SDR, including content that is HDR. As a result, during HDR content creation, such HDR content may include inadvertent use of unnatural and inconsistent luminance values for light sources and light reflections, because the game content creator cannot actually view the luminance values they are using that are native to HDR on the SDR display device used to create the HDR content. This can result in a lower quality HDR image when displayed on HDR capable display devices.

<NPL>) discloses the enhancement of bright video features for HDR displays.

<CIT> and <CIT> disclose methods for image highlight detection and rendering.

In one aspect, the present invention provides a method according to claim <NUM>. In another aspect, the present invention provides a system according to claim <NUM>. In a still further aspect, the present invention provides a computer-readable memory device according to claim <NUM>. Certain more specific aspects of the invention are set out in the dependent claims.

Methods, systems and computer program products are described herein that enable detecting bright regions in HDR content that have incorrect and/or inconsistent tones, and automatically or manually correcting such tones. A bright region is identified in an image. The bright region is classified into an assigned classification. A luminance value of the bright region is determined and compared to predefined luminance values corresponding to the classification. The luminance value of the bright region is adjusted to match the predefined luminance values where there is a mismatch. Bright regions including mismatched or incorrect luminance values may be rendered on display to include a visual indicator that such regions include mismatched luminance values.

A manually input luminance correction may be received for such mismatched bright regions, or a correction may be automatically generated. Such correction may be applied to the HDR image to produce a corrected HDR image. Corrected luminance values may be generated that match another bright region with the same classification in the same image. In addition to generating corrections to incorrect luminance values, a scale adjustment may be applied to luminance values across the bright region to generate a scaled and adjusted luminance value.

Identification and classification of the bright regions of the image may be performed in various ways, such as by a suitably trained machine learning model. Such a model may be trained using images including bright regions having the classifications that may be employed in the HDR content (e.g., sun, moon, fires, explosions, specular highlights etc.). The predefined luminance values for each class of bright region may likewise be determined by a suitably trained machine learning model. Such a model would be trained not only with images of the expected classification, but also having luminance values is the desired range for such classification. Alternatively, predefined luminance values may be manually defined by a technical artist, game developer, other content creator, or other person.

Further features and advantages, as well as the structure and operation of various examples, are described in detail below with reference to the accompanying drawings. It is noted that the ideas and techniques are not limited to the specific examples described herein. Such examples are presented herein for illustrative purposes only. Additional examples will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein.

The features and advantages of embodiments will become more apparent from the detailed description set forth below when taken in conjunction with the drawings, in which like reference characters identify corresponding elements throughout.

The following detailed description discloses numerous embodiments. The scope of the present patent application is not limited to the disclosed embodiments, but also encompasses combinations of the disclosed embodiments, as well as modifications to the disclosed embodiments.

Further, when a feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to effect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described.

As mentioned above, HDR content is often created using SDR display devices and in fact, most video game content today is created using SDR displays. In such case, during content creation, the content creator of HDR content cannot necessarily see the differences between tones on the SDR display, resulting in unnatural and inconsistent luminance values possibly being accidentally used for light sources and light reflections. This may result in a poor quality HDR image. In particular, a viewer of the content on an HDR display may view unexplained differences in tones between content features (e.g., two fires) displayed in a same image, degrading the image from the perspective of the viewer.

The design of HDR content on SDR display devices can lead to further problems. For instance, some content creation tools may unnaturally boost the luminance values of some image features by applying linear scalar values in order to accentuate certain light sources and visual effects (VFX). Still further, different content creators may use different luminance values for the same type of light sources and VFX. Examples are:.

The end result is that even though the final image presented on an SDR display device may look correct, even with the inconsistent and unnatural luminance values included, the same image may look very unnatural and/or inconsistent on an HDR display device.

To correct these problems, content creators and producers spend a lot of extra time reviewing content, such as a video game, on different HDR displays to identify inconsistent and unnatural luminance values. In general, the entire review process is very time consuming. For example, in the case of a video game, content creators and producers have to play through most of the game levels, or in the case of a movie, most of the movie will have to be watched, and the video game or movie may not be completed in its entirety until long after the images contributed by a particular content creator/producer have been created. Subsequently created content for the video game or movie may be created with different luminance values. Accordingly, embodiments enable more efficient HDR content review and luminance correction that overcome these issues. A high-level overview of an HDR content reviewing process in a video game context, according to an example embodiment, is now be discussed.

The HDR content reviewing process begins with a graphic artist, game developer, or other content creator creating HDR content. Typically, the content creator selects one or more objects in a scene to be rendered with HDR luminance values, and defines the luminance values to be applied at run-time. After the creation of HDR objects in a scene is completed, the video game may be run with the newly created HDR content. At run-time within the video game, the luminance values defined during content creation may be applied to the HDR objects in the scene (and may also be used to render additional visual effects as required) to render a final HDR game image (i.e., the final image as displayed on the screen). Embodiments of an automated real-time HDR content reviewer may receive the final HDR game image and may operate as follows.

In an embodiment, the final HDR game image is input to a suitably trained machine learning model to identify the location, size, and luminance value of bright areas in the final HDR game image. In embodiments, the same machine learning model classifies such bright areas. That is, the model determines what each bright area is depicting with respect to a pre-identified category (i.e., a fire, sun, moon, headlight, etc.). Embodiments of the automated real-time HDR content-reviewer may then compare the determined luminance value for each bright area with a predefined luminance value corresponding to the determined classification. For example, in an embodiment, a list of predefined luminance values for various HDR object classifications may be provided to the automated real-time HDR content reviewer. A mismatch between the determined luminance value for each object and the predefined luminance value for objects of that classification indicates the object as rendered may have unnatural and/or inconsistent luminance values.

Embodiments are further enabled to render the final HDR game image with visual indicators superimposed on the unnatural and inconsistent HDR objects. Thereafter, embodiments may stop processing the HDR images and return control to the content creator to determine the next course of action (e.g., manual correction of the object luminance values). In another embodiment, however, the luminance values of the unnatural and inconsistent HDR objects may be automatically adjusted to match the predefined value corresponding to the HDR object classification, and a preview of the final HDR game image rendered on the display (along with the visual indicators to flag such objects for review). Thereafter, the content creator may examine the preview rendered of the HDR objects and determine whether the automatic luminance adjustments should be accepted, rejected, or modified.

These and further embodiments of a real-time HDR content reviewer may be implemented in various ways. For example, <FIG> depicts an example HDR content review system <NUM> including an HDR luminance corrector <NUM>, according to an embodiment. As shown in <FIG>, system <NUM> includes a computing device <NUM> and a display device <NUM>. Computing device <NUM> includes a content editor tool <NUM> executing on the computing device which in turn includes a luminance corrector <NUM>. Other structural and operational embodiments will be apparent to persons skilled in the relevant art(s) based on the following discussion regarding system <NUM> of <FIG>.

A general description of embodiments follows herein below described in the context of system <NUM> and computing device <NUM> of <FIG>. It should be understood that although described in terms of a computing device <NUM> operated by a user, embodiments are not limited to that scenario or computing hardware. For example, embodiments may operate semi or completely autonomously in a variety of hardware environments including, but not limited to, graphics processing units ("GPUs") and other types of computing hardware. It should also be noted that although description of embodiments herein is often couched in terms of HDR images and video processed in the context of video games, embodiments may usefully be employed to review and correct luminance values in any type of HDR content.

Computing device <NUM> of system <NUM> may include any type of computing device whether mobile or stationary, such a desktop computer, a server, a video game console, etc. Computing device <NUM> may be any type of mobile computing device (e.g., a Microsoft® Surface® device, a personal digital assistant (PDA), a laptop computer, a notebook computer, a tablet computer such as an Apple iPad™, a netbook, etc.), a mobile phone (e.g., a cell phone, a smart phone such as a Microsoft Windows® phone, an Apple iPhone®, a phone implementing the Google® Android™ operating system, etc.), a wearable computing device (e.g., a head-mounted device including smart glasses such as Google® Glass™, Oculus Rift® by Oculus VR, LLC, etc.), a stationary computing device such as a desktop computer or PC (personal computer), a gaming console/system (e.g., Microsoft Xbox®, Sony PlayStation®, Nintendo Wii® or Switch®, etc.), etc..

In an embodiment, content editor tool <NUM> executing on computing device <NUM> enables a content editor, game developer, or other qualified personnel to perform a review of HDR content for mismatched luminance values of the nature described herein above. In an embodiment, luminance corrector <NUM> included in content editor tool <NUM> enables manual, semi-automated or fully automatic review and correction of incorrect luminance values in the following general manner.

As an initial matter, video or still images including HDR content for review may be provided to content editor tool <NUM> of computing device <NUM> for display and review on display device <NUM>. For example, HDR image <NUM> may be loaded or otherwise accessed by content editor tool <NUM>, and rendered on display device <NUM>. Once accessible by luminance corrector <NUM> of content editor tool <NUM>, HDR video and/or images such as HDR image <NUM> may have incorrect luminance values detected and corrected in embodiments by operation of the following general steps as discussed in turn below:.

Embodiments of luminance corrector <NUM> of <FIG> may be configured in various ways to perform such operations. For example, <FIG> depicts an embodiment of luminance corrector <NUM>. As shown in <FIG>, luminance corrector <NUM> includes an image classifier <NUM>, a luminance analyzer <NUM>, an image renderer <NUM>, a luminance adjuster <NUM>, a logger <NUM> and a storage component <NUM>. Luminance corrector <NUM> is described in further detail as follows.

In embodiments, HDR image <NUM> is received by image classifier <NUM>. Image classifier <NUM> is configured to thereafter identify and classify bright regions contained in images (including video), such as HDR image <NUM>. As an initial matter, embodiments of image classifier <NUM> must determine whether there is a bright region in a scene, where the region is located, its size, and an identification of the bright region (e.g., a campfire such as bright region <NUM>). Image classifier <NUM> may be configured in various ways to perform these functions, including performing such identification and classification according to a machine vision, image recognition, or other algorithm for identifying objects in images. Examples of such algorithms include convolutional neural networks, appearance-based methods (e.g., edge matching, divide-and-conquer search, greyscale matching, histograms of receptive field responses, large model bases), feature-based methods (e.g., interpretation trees, pose consistency, pose clustering, invariance, geometric hashing, scale-invariant feature transform (SIFT), speeded up robust features (SURF)), genetic algorithms, etc. As described in more detail below, machine learning ("ML") algorithms may be usefully employed for such identification and classification tasks. In embodiments, a suitably trained machine learning model included in image classifier <NUM> may be provided with the image under inspection (e.g., HDR image <NUM>), and produce a list of bright regions <NUM>, including their location, size and classification. Example HDR and SDR images of embodiments will now briefly be discussed, and discussion of luminance corrector <NUM> as depicted in <FIG> will continue thereafter below.

For example, <FIG> which depicts example HDR content image <NUM> including content items <NUM> and <NUM> having the same classification. In particular, content items <NUM> and <NUM> are each a bright region in content image <NUM> and are both classified as "fires. " As shown in <FIG>, content item <NUM> was created to have an average fire HDR brightness of <NUM> nits. Content item <NUM>, on the other hand, was created by a different content creator to have an average fire HDR brightness of <NUM> nits. As discussed above, these HDR brightness values represent the intended brightness for each portion of content when natively displayed on an HDR capable display device. The difference between <NUM> and <NUM> nits of brightness is excessive such that this difference would be noticed by a human viewer on an HDR display device. Such mismatches in content brightness in an image may arise, as mentioned above, due to the use of SDR displays by each content creator.

Consider, for example, <FIG> which depicts an SDR rendering of the example HDR content image <NUM> of <FIG> into an SDR image <NUM> after SDR tone mapping is applied, according to an example embodiment. Because SDR display devices are not physically capable of displaying the full range of luminance values that may be encoded in an HDR image, the luminance values of the image must be scaled to fit in the dynamic range of the SDR display device. This scaling process is generally referred to as tone mapping. Scaled HDR content <NUM> and <NUM> of <FIG> illustrate what content items <NUM> and <NUM> may look like after SDR tone mapping, and when displayed on an SDR display device. Although tone mapped SDR content <NUM> and <NUM> do indeed have visibly different luminance values (i.e., SDR content <NUM> appears slightly less bright than SDR content <NUM>), the differences may not be large enough to be noticeable on an SDR display, or may be within a tolerable range of differences for content of the same classification. For this reason, it may appear to one or both of the creators of content items <NUM> and <NUM> that the luminance values are satisfactory, and/or are close enough to each other to be essentially the same. The problem arises, however, when HDR content items <NUM> and <NUM> are displayed as shown in <FIG> where each would appear substantially different from one another.

Returning now to the discussion of luminance corrector <NUM> as depicted in <FIG>, please recall that image classifier <NUM> is configured to identify and classify bright regions in HDR image <NUM>, and produce a list of the bright regions, referred to as bright regions <NUM>, which indicates the location (e.g., by upper leftmost pixel coordinates, center pixel coordinates, etc.), size (e.g., a rectangle or other shape identified as a two-dimensional array of pixel coordinates), and classification of each bright region (e.g., by classification name, classification identifier, etc.).

In embodiments, the list of bright areas <NUM> is received by luminance analyzer <NUM>. Each of the bright areas indicated by bright areas <NUM> is analyzed by luminance analyzer <NUM> to determine an overall luminance value for the bright area. Luminance analyzer <NUM> may be configured to perform such analysis in numerous ways, including histogram analysis of color values, operations on monochromatic values, averaging values, and so forth. For example, embodiments may set the overall luminance value according to the peak RGB (red-green-blue) value of the bright region (i.e., largest of R G or B values) or the peak average value (i.e. largest of R+B+G/<NUM> for each pixel). Alternatively, embodiments may plot a histogram of luminance values in the bright region and set the luminance value for that region to be the median value of the histogram. In other embodiments, perceptual adjustment factors may also be applied to account for the non-linearity of human perception of brightness.

After determining the luminance values for each bright region, embodiments of luminance analyzer <NUM> are configured to compare each determined luminance value with a predefined luminance value corresponding to the bright region classification as reflected in the list of bright regions <NUM>. For example, a luminance value may be predefined for each class of bright region that may be encountered in the HDR content. In particular, a game designer or content editor may predefine luminance values for, e.g., a fire, explosion or sun to be <NUM>, <NUM> or <NUM> nits, respectively. Thus, embodiments of luminance corrector <NUM> may detect bright region <NUM> of HDR image <NUM>, classify bright region <NUM> as a fire, and determine its luminance value to be <NUM> nits. In this example, upon comparing the value of <NUM> nits with the predefined value of <NUM> nits, embodiments of luminance analyzer <NUM> determines the luminance for that bright region for correction. After performing the above described comparison on each detected and classified bright region of bright regions <NUM>, luminance analyzer <NUM> is configured to generate a list of bright regions requiring correction as incorrect bright regions <NUM>, along with their locations and dimensions.

After determining the bright regions in need of correction, embodiments may perform corrections in a number of ways. For example, in one embodiment, image renderer <NUM> receives incorrect bright regions <NUM>, and on a display screen (e.g., on display device <NUM> of <FIG>), renders a visualization atop the bright regions (e.g., atop bright region <NUM> in <FIG>) to flag such regions for review by a content creator or content editor. Such a visualization may have any suitable form, including a semi-transparent highlighting over the bright region, an appropriately colored bounding box around the bright region, etc. In an embodiment, in addition to rendering visualizations atop the flagged bright regions, embodiments may also apply a provisional or suggested luminance adjustment automatically to the bright regions. Alternatively, image renderer <NUM> may be configured to provide suggested luminance values <NUM> to luminance adjuster <NUM>.

In embodiments, luminance adjuster <NUM> may be configured to operate in different ways depending on the configuration of luminance corrector <NUM>. For example, luminance adjuster <NUM> may be configured to automatically and permanently apply the provisional or suggested luminance values to the bright regions to produce corrected image <NUM> including corrections for each bright region. Alternatively, luminance adjuster <NUM> may be configured to permit review of the previously applied provisional/suggested luminance values, and permit such changes to be rejected or accepted. The latter alternative may be performed for various reasons, such as the machine learning model of image classifier <NUM> mis-identifying portions of the image as having incorrect luminance values, for artistic reasons why such provisional/suggested luminance values should not be accepted (e.g., when the suggested luminance adjustments are based on physically realistic luminance values, but the HDR image under review is intended to appear "cartoony"), etc. In another embodiment of luminance adjuster <NUM>, suggested luminance values <NUM> may not be applied to the HDR image as a preview, but instead be presented to the content editor as a suggested luminance value when accepting a manually input luminance correction value from a content editor or developer. Whether correcting luminance values automatically or via manual input, luminance adjuster <NUM> is configured to apply the corrected luminance value to render a corrected image <NUM>.

In another embodiment, luminance corrector <NUM> may be configured to operate as an automated image test tool. In such an embodiment, HDR images for review may be provided to luminance adjuster <NUM> in bulk, with each image being processed by image classifier <NUM> as described above to generate a list of bright areas <NUM> for input to luminance analyzer <NUM>, which in turn determines incorrect bright regions <NUM>. In this embodiment, however, incorrect bright regions <NUM> may or may not be provided to image renderer <NUM> for an interactive review of the image in the manner described above. Furthermore, luminance analyzer <NUM> may generate a log <NUM> of such bright areas needing correction. Log <NUM> may include the data and metadata regarding each bright region (e.g., location, size, classification, determined brightness), and may include a screenshot of the output of image renderer <NUM> with visualizations as described above. Logger <NUM> is may be configured to receive log <NUM> from luminance analyzer <NUM>, and process log <NUM> for storage in storage component <NUM>, in an embodiment.

Note, although the above description of embodiments of luminance corrector <NUM> is couched in terms of lists of various types, it should be understood that embodiments need not produce or store a literal list, and other types of data structures or means of representing the data described with respect to each abovementioned list may be employed. In embodiments, for example, the abovementioned lists may comprise any number of different data structures, whether in memory, or stored in some fashion. Such data structures may comprise, for example, arrays, associative arrays, linked lists, records, objects (including object methods), stacks, queues or graphs.

Luminance corrector <NUM> may operate in various ways to perform its functions. For instance, <FIG> depicts a flowchart <NUM> of a method for reviewing and correcting HDR content, according to an embodiment. <FIG> is described with continued reference to <FIG> and <FIG>. However, other structural and operational embodiments will be apparent to persons skilled in the relevant art(s) based on the following discussion regarding flowchart <NUM> and luminance corrector <NUM> of <FIG> and <FIG>.

As shown in <FIG>, flowchart <NUM> begins at step <NUM>. In step <NUM>, in an image, a portion of that image that is a bright region is identified. For example, and with continued reference to <FIG>, image classifier <NUM> may identify in an image, such as HDR image <NUM>, one or more bright regions that are portions of the image. As described in above, image classifier <NUM> may be configured to perform such identification with the use of a suitably trained machine learning model (that is discussed in greater detail herein below). For example, image classifier <NUM> may identify bright regions <NUM> and <NUM> in image <NUM>.

In step <NUM>, the identified bright region is classified into an assigned classification of a set of predetermined classifications. For example, and with continued reference to <FIG>, image classifier <NUM> may be configured to perform classification of bright regions identified in HDR image <NUM> as discussed above, and in further detail below.

Flowchart <NUM> of <FIG> continues with step <NUM>. In step <NUM>, a luminance value of the bright region is determined. For example, image classifier <NUM> of <FIG> may provide luminance analyzer <NUM> with a list of bright regions (including their location, size and classification) in bright regions <NUM>. As discussed above, luminance analyzer <NUM> is configured to determine a luminance value for each identified and classified bright region.

In step <NUM>, the determined luminance value is compared to a predefined luminance value corresponding to the assigned classification. As discussed above with reference to <FIG>, luminance analyzer <NUM> is configured to compare the determined luminance values of the bright regions with predefined luminance values corresponding to the assigned classification. That is, per the example discussed above, luminance analyzer <NUM> may configured to compare the luminance value determined for a "fire" with the predefined value of <NUM> nits. As shown in <FIG>, luminance analyzer <NUM> generates incorrect bright regions <NUM>, which includes identified bright regions having luminance values that do not match (e.g., exactly, or within a predetermined threshold) the predetermined luminance values for the classifications of the identified bright regions.

Flowchart <NUM> of <FIG> continues with step <NUM>. In step <NUM>, the determined luminance value is adjusted to an adjusted luminance value for the bright region based on said comparing. For example, and as discussed above with reference to <FIG>, luminance adjuster <NUM> is configured to, either automatically or with manual assistance, adjust the luminance values of bright regions identified in incorrect bright regions <NUM> as needing correction by luminance analyzer <NUM>.

Flowchart <NUM> of <FIG> concludes with step <NUM>. In step <NUM>, the image with the bright region having the adjusted luminance value is rendered. For example, and with continued reference to <FIG>, luminance adjuster <NUM> may be configured to apply the necessary luminance adjustments to generate corrected image <NUM>, that may in turn be provided to image renderer <NUM> for rendering.

In the foregoing discussion of steps <NUM>-<NUM> of flowchart <NUM>, it should be understood that at times, such steps may be performed in a different order or even contemporaneously with other steps. For example, the identifying and classifying of steps <NUM> and <NUM>, respectively, may be performed simultaneously by the same machine learning model. Other operational embodiments will be apparent to persons skilled in the relevant art(s). Note also that the foregoing general description of the operation of luminance corrector <NUM> is provided for illustration only, and embodiments of luminance corrector <NUM> may comprise different hardware and/or software, and may operate in manners different than described above. Indeed, steps of flowchart <NUM> may be performed in various ways.

For example, <FIG> depicts a flowchart of refinements to the method for reviewing and correcting HDR content as depicted in flowchart <NUM> of <FIG>, according to an embodiment. <FIG> is described with continued reference to <FIG>, <FIG> and <FIG>. However, other structural and operational embodiments will be apparent to persons skilled in the relevant art(s) based on the following discussion regarding flowchart <NUM> and <FIG>, <FIG> and <FIG>.

As shown in <FIG>, flowchart <NUM> begins at step <NUM>. In step <NUM>, prior to adjusting the determined luminance value to an adjusted luminance value, the determined luminance value is determined to be incorrect based on a difference from the predefined luminance value. For example, and with continued reference to <FIG>, analyzer <NUM> is configured to compare the luminance values determined at step <NUM> with predefined luminance values corresponding to the assigned classification. In an embodiment, luminance analyzer <NUM> may be configured to determine the difference between the luminance values determined for a given bright region, and the predefined luminance value corresponding to the assigned classification. Luminance analyzer <NUM> may use the determined difference to determine that the luminance value for associated bright region is incorrect where, for example, the determined difference exceeds a predefined threshold either in absolute or percentage terms.

At step <NUM>, a visual indicator is rendered on the image in a location corresponding to the identified bright region in the process of adjusting the determined luminance value. For example, and with continued reference to <FIG>, image renderer <NUM> is configured to render a visual indicator in a location corresponding to incorrect bright regions <NUM> identified by luminance analyzer <NUM>. Such a visual indicator may comprise, for example, a bounding box around the bright region or some other means of illustrating where a correction is needed.

Flowchart <NUM> concludes at step <NUM>. In step <NUM>, a manually corrected luminance value (i.e., a luminance value accepted via manual input to a user interface from a graphic artist, game developer, other content creator, or other person) is received for the identified bright region as the adjusted luminance value in the process of adjusting the determined luminance value. For example, and as discussed in detail above in relation to <FIG>, luminance adjuster <NUM> of luminance corrector <NUM> is configured to accept either a manually corrected luminance value for each identified bright region, or an automatically generated corrected luminance value. The automatically generated corrected luminance value may, in an embodiment, be used to preview suggested image changes which then may be manually accepted, rejected or altered.

<FIG> depicts a flowchart <NUM> of a method for automatically determining the adjusted luminance value for the identified bright region, according to an embodiment. <FIG> is described with continued reference to <FIG>, <FIG> and <FIG>. However, other structural and operational embodiments will be apparent to persons skilled in the relevant art(s) based on the following discussion regarding flowchart <NUM> and <FIG>, <FIG> and <FIG>.

Flowchart <NUM> of <FIG> includes step <NUM>. In step <NUM>, prior to adjusting the determined luminance value to an adjusted luminance value, the adjusted luminance value for the identified bright region is automatically determined. For example, and as discussed in detail above in relation to flowchart <NUM> of <FIG>, luminance adjuster <NUM> of luminance corrector <NUM> is configured to accept either a manually corrected luminance value for each identified bright region, or an automatically generated corrected luminance value. The automatically generated corrected luminance value may, in an embodiment, be used to preview suggested image changes which then may be manually accepted, rejected or altered. The automatically generated corrected luminance value may, in an embodiment, be generated to match the predefined luminance values that were used by luminance analyzer <NUM> to determine that the bright region needed correction (i.e., the luminance value associated with the classification).

As described above, image classifier <NUM> may use any of a variety of algorithms for identifying and classifying bright regions. For instance, <FIG> depicts a flowchart <NUM> of a method for a machine learning model to identify and classify bright regions, according to an embodiment. <FIG> is described with continued reference to <FIG>, <FIG> and <FIG>. However, other structural and operational embodiments will be apparent to persons skilled in the relevant art(s) based on the following discussion regarding flowchart <NUM> and <FIG>, <FIG> and <FIG>.

Flowchart <NUM> of <FIG> includes step <NUM>. In step <NUM>, the displayed image is provided to a machine learning model to identify a location and a size of the bright region, the machine learning model trained to identify bright regions. For example, and with continued reference to <FIG>, image classifier <NUM> may be configured to identify the location and size of bright regions within HDR image <NUM> through the use of a suitably trained machine learning model as discussed in more detail herein below.

A trained machine learning model suitable for use by image classifier <NUM> may be generated in various ways. For instance, to generate such a model, a video game may be executed in a machine learning (ML) application, such as TensorFlow™, to generate training data that includes the video stream (or some subset of frames of the video stream) of the video game. Alternatively, movies or other content including HDR content may be played back within the ML application to train a machine learning model. The training phase generates a machine learning model capable of identifying and classifying bright areas in images during live game play, or based on still images excerpted therefrom, or otherwise provided. Alternatively, a machine learning model may be trained on still images excerpted from video games or that otherwise contain bright areas of various positions, luminance intensities and classifications.

The video stream and/or still images provided to the ML application are typically accompanied by other data or metadata ("training indications") that identify the regions of interest in each frame or image (i.e. the classes of HDR content that one wishes the ML model to be able to detect and classify once trained). For instance, training indications may identify the locations, intensities and classification of light sources such as the sun, the moon, fires, explosions, specular highlights, headlights, taillights, license plates and so forth, within each image/frame. In sum, all the various types of light sources in the game, including reflections of the light sources on different types of objects, should be included in the training set. Light sources such as these or others that are determined to be important to particular game scenarios may be flagged during the training phase by a content creator/game developer user (or automatically), such as by indicating their location in the a frame of video or image (e.g., by the user indicating an object's location by a point, by drawing a box around the object, etc.).

In an embodiment, a ML application may be configured to receive and process the training video and/or still images, along with corresponding training indications, to train a machine language model. The ML application may use any suitable techniques to generate the model, including supervised ML model generation algorithms such as supervised vector machines (SVM), linear regression, logistic regression, naive Bayes, linear discriminant analysis, decision trees, k-nearest neighbor algorithm, neural networks, etc. In an embodiment, the generated model is capable of providing a confidence level indicative of whether a specific class of bright region is identified in a video frame or still image.

After obtaining a suitably trained ML model, embodiments of image classifier <NUM> (which incorporates the ML model) are provided to the model with scenes, video or still images under development (e.g., HDR image <NUM>) in order to detect and classify the bright areas in the final HDR game image. In an embodiment, and as discussed above, a list <NUM> of all such detected and classified bright areas (including detail regarding the classification (e.g., fire vs sun vs headlight, etc.), position and size of the bright areas) may be provided to luminance analyzer <NUM>.

<FIG> depicts a flowchart <NUM> of a method for classifying the identified bright region by the machine learning model, according to an embodiment. <FIG> is described with continued reference to <FIG>, <FIG> and <FIG>. However, other structural and operational embodiments will be apparent to persons skilled in the relevant art(s) based on the following discussion regarding flowchart <NUM> and <FIG>, <FIG> and <FIG>.

Flowchart <NUM> of <FIG> includes step <NUM>. In step <NUM>, the identified bright region is classified by the machine learning model. For example, and as discussed immediately above, image classifier <NUM> may incorporate a suitably trained machine learning model to perform not only identification of the location and size of bright regions, but also to classify such regions according to their type (i.e., fire vs sun vs headlight, etc.).

Content editor tool <NUM>, luminance corrector <NUM>, image classifier <NUM>, luminance analyzer <NUM>, image renderer <NUM>, luminance adjuster <NUM>, logger <NUM>, and flowcharts <NUM>, <NUM>, <NUM>, <NUM> and/or <NUM> may be implemented in hardware, or hardware combined with software and/or firmware. For example, content editor tool <NUM>, luminance corrector <NUM>, image classifier <NUM>, luminance analyzer <NUM>, image renderer <NUM>, luminance adjuster <NUM>, logger <NUM>, and flowcharts <NUM>, <NUM>, <NUM>, <NUM> and/or <NUM> may be implemented as computer program code/instructions configured to be executed in one or more processors and stored in a computer readable storage medium. Alternatively, content editor tool <NUM>, luminance corrector <NUM>, image classifier <NUM>, luminance analyzer <NUM>, image renderer <NUM>, luminance adjuster <NUM>, logger <NUM>, and flowcharts <NUM>, <NUM>, <NUM>, <NUM> and/or <NUM> may be implemented as hardware logic/electrical circuitry.

For instance, in an embodiment, one or more, in any combination, of content editor tool <NUM>, luminance corrector <NUM>, image classifier <NUM>, luminance analyzer <NUM>, image renderer <NUM>, luminance adjuster <NUM>, logger <NUM>, and flowcharts <NUM>, <NUM>, <NUM>, <NUM> and/or <NUM> may be implemented together in a SoC. The SoC may include an integrated circuit chip that includes one or more of a processor (e.g., a central processing unit (CPU), microcontroller, microprocessor, digital signal processor (DSP), etc.), memory, one or more communication interfaces, and/or further circuits, and may optionally execute received program code and/or include embedded firmware to perform functions.

<FIG> depicts an exemplary implementation of a computing device <NUM> in which embodiments may be implemented. For example, content editor tool <NUM>, luminance corrector <NUM>, image classifier <NUM>, luminance analyzer <NUM>, image renderer <NUM>, luminance adjuster <NUM> and logger <NUM> may each be implemented in one or more computing devices similar to computing device <NUM> in stationary or mobile computer embodiments, including one or more features of computing device <NUM> and/or alternative features. The description of computing device <NUM> provided herein is provided for purposes of illustration, and is not intended to be limiting. Embodiments may be implemented in further types of computer systems, as would be known to persons skilled in the relevant art(s).

A number of program modules may be stored on the hard disk, magnetic disk, optical disk, ROM, or RAM. These programs include operating system <NUM>, one or more application programs <NUM>, other programs <NUM>, and program data <NUM>. Application programs <NUM> or other programs <NUM> may include, for example, computer program logic (e.g., computer program code or instructions) for implementing content editor tool <NUM>, luminance corrector <NUM>, image classifier <NUM>, luminance analyzer <NUM>, image renderer <NUM>, luminance adjuster <NUM>, logger <NUM>, and flowcharts <NUM>, <NUM>, <NUM>, <NUM> and/or <NUM> (including any suitable step of flowcharts <NUM>, <NUM>, <NUM>, <NUM> and/or <NUM>), and/or further embodiments described herein.

Such computer programs, when executed or loaded by an application, enable computing device <NUM> to implement features of embodiments described herein.

The present invention provides a method according to claim <NUM>. The method includes: identifying in an image a bright region that is a portion of the image; classifying the identified bright region into an assigned classification of a set of predetermined classifications; determining a luminance value of the identified bright region; comparing the determined luminance value to a predefined luminance value corresponding to the assigned classification and determining the determined luminance value is incorrect based on a difference from the predefined luminance value; adjusting a luminance value of the identified bright region to an adjusted luminance value based on said comparing; and rendering the image with the identified bright region having the adjusted luminance value.

In one embodiment of the method, said adjusting further comprises: rendering a visual indicator on the image in a location corresponding to the identified bright region; and receiving a manually corrected luminance value for the identified bright region as the adjusted luminance value.

In an additional embodiment of the method, adjusting comprises automatically determining the adjusted luminance value for the identified bright region.

In one embodiment of the method, adjusting comprises adjusting a luminance value of the identified bright region to an adjusted luminance value that is a same luminance value for a second bright region in the image having the assigned classification.

In another embodiment of the method, rendering comprises applying a linear scale to the adjusted luminance value to account for a visual effect in the displayed image to generate a scaled and adjusted luminance value; and rendering the displayed image with the identified bright region having the scaled and adjusted luminance value.

In an additional embodiment of the method, identifying comprises providing the displayed image to a machine learning model to identify a location and a size of the bright region, the machine learning model trained to identify bright regions.

In one embodiment of the method, classifying comprises classifying the identified bright region by the machine learning model.

In another embodiment of the method, the method further comprises logging information of the identified bright region and information of at least one other bright region in the image for which an adjusted luminance value is determined.

The present invention further provides a system according to claim <NUM>. The system comprises: one or more processor circuits; one or more memory devices connected to the one or more processor circuits, the one or more memory devices storing: computer program logic for execution by the one or more processor circuits, the computer program logic comprising: an image classifier configured to: identify in a displayed image a bright region that is a portion of the displayed image, and classify the identified bright region into an assigned classification of a set of predetermined classifications; a luminance analyzer configured to determine a luminance value of the identified bright region, and determine a comparison between the determined luminance value and a predefined luminance value corresponding to the assigned classification and to determine the determined luminance value is incorrect based on a difference from the predefined luminance value; a luminance adjuster configured to adjust a luminance value of the identified bright region to an adjusted luminance value based on said comparison; and an image renderer configured to render the image with the identified bright region having the adjusted luminance value.

In one embodiment of the system, the luminance adjuster is configured to adjust a luminance value of the identified bright region by receiving a manually corrected luminance value for the identified bright region as the adjusted luminance value.

In another embodiment of the system, the luminance adjuster is configured to automatically determine the adjusted luminance value for the identified bright region.

In an additional embodiment of the system, the luminance adjuster is configured to adjust a luminance value of the identified bright region to an adjusted luminance value that is a same luminance value for a second bright region in the image having the assigned classification.

In one embodiment of the system, the image renderer is further configured to apply a linear scale to the adjusted luminance value to account for a visual effect in the displayed image to generate a scaled and adjusted luminance value; and render the displayed image with the identified bright region having the scaled and adjusted luminance value.

In another embodiment of the system, identifying comprises providing the displayed image to a machine learning model to identify a location and a size of the bright region, the machine learning model trained to identify bright regions.

In an additional embodiment of the system, classifying comprises classifying the identified bright region by the machine learning model.

In one embodiment of the system, the system further comprises a logger configured to log information of the identified bright region and information of at least one other bright region in the image for which an adjusted luminance value is determined.

Claim 1:
A method, comprising:
identifying (<NUM>) in an image (<NUM>) a bright region (<NUM>) that is a portion of the image (<NUM>);
classifying (<NUM>) the identified bright region (<NUM>) into an assigned classification of a set of predetermined classifications;
determining (<NUM>) a luminance value of the identified bright region (<NUM>);
comparing (<NUM>) the determined luminance value to a predefined luminance value corresponding to the assigned classification and determining whether the determined luminance value is incorrect based on a difference from the predefined luminance value, an incorrect luminance value being a luminance value which does not match the predefined luminance value to within a predetermined threshold;
adjusting (<NUM>) a luminance value of the identified bright region (<NUM>) to an adjusted luminance value based on said comparing; and
rendering (<NUM>) the image with the identified bright region having the adjusted luminance value.