Apparatus and method for reducing blue light

Abstract

According to various, but not all, embodiments, an apparatus is provided including components for binocularly displaying visual content as a first image directed to a user's first eye and a second image directed to a user's second eye. The first image includes a first region in which the blue spectral component of the visual content is reduced compared to a corresponding first region of the second image. The second image includes a second distinct region in which the blue spectral component of the visual content is reduced compared to a corresponding second region of the first image.

Description

Technical Field

[0001] Embodiments of this disclosure relate to blue light reduction. Some relate to blue light reduction for displayed visual content. Background Technology

[0002] Light from screens can disrupt a person's natural circadian rhythm, especially when used at night.

[0003] Blue light filters reduce sleep loss and circadian rhythm disruption caused by screen time by filtering out the high-energy portion of the visible spectrum. Summary of the Invention

[0004] According to various, but not all, embodiments, an apparatus is provided including components for binocularly displaying visual content as a first image directed to a user's first eye and a second image directed to a user's second eye. The first image includes a first region in which the blue spectral component of the visual content is reduced compared to a corresponding first region of the second image. The second image includes a second distinct region in which the blue spectral component of the visual content is reduced compared to a corresponding second region of the first image.

[0005] According to various, but not all, embodiments, a method is provided that includes binocularly displaying visual content as a first image pointed to a user's first eye and a second image pointed to a user's second eye. The first image includes a first region in which the blue spectral component of the visual content is reduced compared to a corresponding first region of the second image. The second image includes a second distinct region in which the blue spectral component of the visual content is reduced compared to a corresponding second region of the first image.

[0006] According to various, but not all, embodiments, a computer program is provided that, when run on a computer, performs the following: causing visual content to be displayed binocularly as a first image pointed to a user's first eye and a second image pointed to a user's second eye. The first image includes a first region in which the blue spectral component of the visual content is reduced compared to a corresponding first region of the second image. The second image includes a second distinct region in which the blue spectral component of the visual content is reduced compared to a corresponding second region of the first image.

[0007] The following sections of this 'Summary of the Invention' section describe various features that may be characteristic of any embodiment described in the preceding sections of this 'Summary of the Invention' section. Additionally, the description of the function should be considered as also disclosing any components suitable for performing that function.

[0008] A first spatial discontinuous spectral filter can be applied to form a first image, and a second different spatial discontinuous spectral filter can be applied to form a second image.

[0009] The first and second filters can be mirror filters.

[0010] The first region of the first image can be based on a target region of the retina of the first eye associated with non-image forming (NIF) functions.

[0011] A predefined region of the retina can be selected from multiple different predefined regions of the retina based on the user's characteristics, serving as the target region for the retina of the first eye. These multiple predefined regions of the retina are associated with different characteristics.

[0012] Data mapping the locations of intrinsically light-sensitive retinal ganglion cells (ipRGCs) in the retina of the first eye can be received. The target area of ​​the retina of the first eye can be determined based on said data.

[0013] The values ​​of the parameters that parameterize the Non-Image Formation (NIF) function can be measured. Training data is formed by pairing parameter values ​​with data representing images displayed during a defined time period prior to the parameter values ​​being measured. The training data enables the generation of a model of the retina of the user's first eye. The definition of the target region of the retina of the first eye associated with the NIF function can be obtained from the model.

[0014] The first region of the first image can be based on the position of the user's first eye relative to the component used for binocular display of visual content.

[0015] The position of the user's first eye can be determined based on the analysis of images captured by a camera with a known position relative to the components used for binocular display of visual content.

[0016] The reduction of the blue spectral component of the visual content in the first region of the first image can be controlled to prevent one or more of the following:

[0017] The instantaneous intensity of blue light exceeds the first threshold; or

[0018] The cumulative intensity of blue light over a given time period exceeds the second threshold.

[0019] User input that allows manual adjustment of the first and / or second thresholds can be accepted.

[0020] The first threshold and / or the second threshold may change in response to changes in environmental conditions and / or user actions.

[0021] The reduction of the blue spectral component of the visual content in the first region of the first image can be controlled to prevent one or more of the following:

[0022] The reduction exceeds the third threshold; or

[0023] The spatial contrast between the first region of the first image and its adjacent regions exceeds a fourth threshold.

[0024] According to various, but not all, embodiments, an apparatus is provided comprising components for controlling visual content of an image as a first image directed to a user's first eye and a second image directed to a user's second eye, wherein the first image includes a spatially confined first region in which high-frequency spectral components of the visual content are reduced compared to a corresponding first region of the second image, and wherein the second image includes a spatially confined second distinct region in which high-frequency spectral components of the visual content are reduced compared to a corresponding second region of the first image.

[0025] Examples claimed in the appended claims are provided according to various, but not all, embodiments. Attached Figure Description

[0026] Some examples will now be described with reference to the accompanying drawings, in which:

[0027] Figure 1 An example of the apparatus described herein is shown;

[0028] Figure 2 Another example of the image described in this article is shown;

[0029] Figure 3 Another example of the controller described in this article is shown;

[0030] Figure 4 Another example of the image described in this article is shown;

[0031] Figure 5 Another example of the filter described in this article is shown;

[0032] Figure 6 Another example of the filter described in this article is shown;

[0033] Figure 7 Another example of the filter adaptation described in this article is shown;

[0034] Figure 8 Another example of the filter adaptation described in this article is shown;

[0035] Figure 9 Another example of the filter adaptation described in this article is shown;

[0036] Figure 10 Another example of the filter adaptation described in this article is shown;

[0037] Figure 11 Another example of the binocular display described in this article is shown;

[0038] Figure 12 Another example of the method described in this paper is shown;

[0039] Figure 13 Another example of the controller described in this article is shown;

[0040] Figure 14 Another example of the delivery mechanism described in this article is shown. Detailed Implementation

[0041] Figure 1 An example of device 100 is illustrated. In this example, but not all examples, device 100 includes a controller 110 and a display 120. The controller 110 is configured to receive an image 102 including visual content and to generate a first image 121 and a second image 122 displayed by the display 120. In other examples, device 100 may include only the controller 110.

[0042] The display 120 is configured to display a first image 121 to the user's first eye 131, such as Figure 2 As illustrated, and configured to display a second image 122 to the user's second eye 132, as shown. Figure 2 As shown in the diagram.

[0043] Display 120 may include one or more displays. In some examples, display 120 may be provided by a head-mounted display device. In other examples, display 120 may be provided by a holographic display.

[0044] A display is a device that controls content perceived (viewed) by a user's vision. Display 120 can be a visual display that selectively provides light to the user. Examples of visual displays include liquid crystal displays, direct retinal projection displays, near-eye displays, holographic displays, etc. The display can be a head-mounted display (HMD), a handheld display, a television display, or some other type of display. In addition to the intensity of each frequency, holographic displays or other light field displays can also control the light emission angle of the pixels.

[0045] Controller 110 is configured to control the binocular display of visual content 102 as a first image 121 directed to the user's first eye 131 and a second image 122 directed to the user's second eye 132, such as Figure 2 As shown in the diagram.

[0046] "Binocular" refers to the simultaneous use of both eyes. The binocular display of visual content 102 allows the user to see both the first image 121 and the second image 122 simultaneously. The first image 121 is seen using the first eye 131 at a first moment, and the second image 122 is seen using the second eye 132 at the same first moment. The second image 122 is not seen by the first eye 131 at the same first moment, and the first image 121 is not seen by the second eye 132 at the same first moment. It should be understood that the simultaneous viewing of the first image 121 and the second image 122 by the user does not necessarily require that they be displayed perfectly simultaneously. Due to persistence of vision, the display of the first image 121 and the second image 122 may be time-shifted, yet they may still be seen simultaneously by the user.

[0047] The first image 121 includes a first image region 11, in which the high-frequency spectral components of the visual content are reduced compared to the corresponding first region 11' of the second image 122. The second image 122 includes a second different region 12, in which the high-frequency spectral components of the visual content are reduced compared to the corresponding second region 12' of the first image 121.

[0048] The reduced high-frequency spectral components can be within a relatively narrow frequency band. These high-frequency spectral components can be blue spectral components. They can be components with frequencies ranging from 460 nm to 484 nm.

[0049] The controller 110 modifies image 102 to generate a first image 121, making the first region 11 a spatially confined region, and modifies image 102 to make the second region 12 a spatially confined region. Similarly, the corresponding first region 11' and the corresponding second region 12' are spatially confined.

[0050] The first region 11 and its corresponding first region 11' are related because they contain visual content with the same characteristics. The second region 12 and its corresponding second region 12' are related because they contain visual content with the same characteristics.

[0051] In some examples, there is no correspondence between the first region 11 and the second region 12, and there is no correspondence between the corresponding first region 11' and the corresponding second region 12'.

[0052] Image 102 can be any suitable image. It can be a still image or a moving image from a camera, such as video or a GUI object. Instead of image 102, visual content can be included in a pair of stereoscopic images 102. In some examples, image 102 is a pixelated image that defines an independent intensity value for each color in the palette for each pixel.

[0053] Figure 3 An example of controller 110 is illustrated. In this example, but not all examples, controller 110 includes a preprocessing block 140 that receives image 102 and provides a first version 141 of image 102 to a first filter 151 and a second version 142 of image 102 to a second filter 152. The first filter 151 produces a first image 121, and the second filter produces a second image 122.

[0054] In some examples, processing block 140 may direct input image 102 to both first filter 151 and second filter 152. In this example, the first version 141 and the second version 142 of input image 102 are identical. Where visual content is included in a pair of stereoscopic images 102, the first version 141 may be the first item in the stereo pair, and the second version 142 may be the second item in the stereo pair. In other examples, input image 102 may be processed to produce the first version 141 and the second version 142. For example, in some, but not all, examples, a horizontal spatial offset (parallax) may be applied between the first version 141 and the second version 142 of image 102 to provide a stereoscopic effect.

[0055] The first filter 151 can be any suitable type of filter, the transform of which is input to its image to produce a first image 121 as output. The first filter 151 can be implemented in any suitable way, such as physical, electronic, or digital.

[0056] The second filter 152 can be any suitable type of filter, the transformation of which is input to its image to produce a second image 122 as output. The second filter 152 can be implemented in any suitable way, such as physical, electronic, or digital.

[0057] Figure 4 The illustration shows an example of visual content being directed towards the user's first eye 131 and second eye 132. From Figure 4 It can be understood that the first image 121 points to the user's first eye 131, and the second image 122 points to the user's second eye 132. From Figure 4 It can be seen that the first image 121 and the second image 122 share the same visual content, which is displayed to the user by both eyes.

[0058] The first image 121 includes a first region 11, in which the blue spectral component of the visual content is reduced compared to the corresponding region 11' of the second image 122. The first region 11 of the first image 121 and the corresponding first region 11' of the second image 122 include the same visual features of the displayed visual content. That is, they are related to the same part of the displayed scene.

[0059] The second image 122 includes a second distinct region 12 in which the blue spectral component of the visual content is reduced compared to the corresponding second region 12' of the first image 121. The second region 12 of the second image 122 and the corresponding second region 12' of the first image 121 include the same visual features of the displayed visual content. That is, they relate to the same part of the displayed scene.

[0060] Figure 5 The diagram illustrates what can be used to generate Figure 4 Examples of first filters 151 and second filters 152 illustrated in the first image 121 and second image 122. The first filter 151 has an attenuation portion 161 aligned with a first portion 11 of the first image 121. The attenuation portion 161 is configured to provide frequency-selective attenuation, reducing high-frequency spectral components such as the blue light component. The first filter 151 does not have an attenuation portion aligned with a corresponding first portion 11' (not shown) of the first image 121.

[0061] The second filter 152 has an attenuation portion 162 aligned with a second portion 12 of the second image 122. The attenuation portion 162 is configured to provide frequency-selective attenuation, which reduces high-frequency spectral components such as the blue light component. The second filter 152 does not have an attenuation portion aligned with a corresponding second portion 12' (not shown) of the second image 122.

[0062] To be from Figure 5 It is understood that the first filter 151 and the second filter 152 are spatially discontinuous because the attenuation portions 161, 162 are spatially constrained. The controller 110 therefore applies the first spatially discontinuous spectral filter 151 to image 102 to produce a first image 121, and applies the second spatially discontinuous spectral filter 152 to image 102 to produce a second image 122.

[0063] To be from Figure 5 It is understood that the difference between the first filter 151 and the second filter 152 is that the attenuation portions 161 and 162 are located differently within filters 151 and 152, respectively, so that they filter different portions of image 102. In the illustrated example, attenuation portion 161 is located in the upper right portion of the first filter 151, and attenuation portion 162 is located in the upper left portion of the second filter 152.

[0064] exist Figure 5 In the example, only a single attenuation portion 161, 162 is illustrated in each of the first filter 151 and the second filter 152. However, for example, as... Figure 6As illustrated, each of the filters 151 and 152 may include multiple attenuation sections 161 and 162.

[0065] exist Figure 6 In the example, the first filter 151 includes attenuation portions 161A, 161B, 161C, and 161D, and each of these attenuation portions 161 produces a modified, spatially constrained region in the first image 121, wherein the blue spectral component of the visual content of the image is reduced. The second filter 152 includes attenuation portions 162A, 162B, 162C, and 162D, and each of these attenuation portions 162 produces a modified, spatially constrained region in the second image 122, wherein the blue spectral component of the visual content of the image is reduced.

[0066] In some, but not all, examples, attenuation portions 161, 162 can have constant attenuation over space-constrained filter regions.

[0067] Alternatively, in some, but not all, examples, the attenuation portions 161, 162 can have variable attenuation over spatially constrained filter regions. For example, the attenuation can be larger towards the center of the filter region and smaller towards the periphery. The boundaries of the filter region can be faded or controlled to avoid boundary detection or other visual processes, which may make the filter effect obvious to the user. In some examples, the fading gradient from the center to the periphery can be controlled.

[0068] The locations of the attenuation portions 161A to 161D in the first filter 151 depend on the corresponding target region of the retina of the user's first eye 131. The corresponding target region of the retina of the user's first eye 131 is associated with non-image forming (NIF) functions.

[0069] The locations of the attenuation portions 162A to 162D in the second filter 152 depend on the corresponding target region of the retina of the user's second eye 132. The corresponding target region of the retina of the user's first eye 131 is associated with the non-image forming (NIF) function.

[0070] The target region includes intrinsically light-sensitive retinal ganglion cells (ipRGCs). ipRGCs play non-image-forming functional roles, such as regulating the body's diurnal functions, including circadian rhythms. ipRGCs are unevenly distributed throughout the retina (the distribution is not necessarily uniform). Figure 5(As shown). Different diurnal functions have been found to be linked to different ipRGC populations, each distributed in the retina. Light incident on a corresponding ipRGC population affects the corresponding diurnal function. ipRGCs also have functions such as mediating pupillary light response, regulating appetite, and regulating mood.

[0071] The uneven distribution of ipRGCs in the retina of the first eye 131 is spatially reflected in the opposing second eye 132 (within the bisecting vertical axis between eyes 131 and 132). Therefore, at least some segments of the image (falling onto the retinal region containing ipRGCs in one eye) will fall onto the opposing region in the other eye that does not contain ipRGCs. Thus, in one eye, a complete or enhanced color image segment (not filtered by attenuation portions 161 and 162) can be displayed in a retinal region without affecting the ipRGCs. In the other eye, the image portion is displayed with a reduced palette (segments filtered by attenuation portions 161 and 162). The two images from the left eye 131 and the right eye 132 are fused in the user's visual cortex, and the two images are superimposed to form a complete image in which aesthetic variations (perceived palette) are reduced and diurnal distortion and other effects of ipRGC exposure are diminished.

[0072] That is, by reducing the blue spectral component of the visual content in the first region 11 of the first image 121 compared to the corresponding first region 11' of the second image 122, and reducing the blue spectral component of the visual content in the second different region 12 of the second image 122 compared to the corresponding second region 12' of the first image 121, the basic full-color palette rendering of the visual content is generated by superimposing the first image 121 and the second image 122 in the visual cortex, and less ipRGC will be exposed to blue light.

[0073] The first filter 151 and the second filter 152 achieve the desired exposure conditions for different retinal regions, avoiding unwanted biological effects while maintaining the viewer's perception of the complete image.

[0074] In at least some examples, the non-uniform distribution of ipRGCs in the retina of the first eye 131 defines the distribution of the attenuation portion 161 of the first filter 151, and the non-uniform distribution of ipRGC cells in the retina of the second eye 132 defines the distribution of the attenuation portion 162 of the second filter 152. Therefore, in this example, the spatial pattern of the attenuation portion 161 of the first filter 151 is a mirror image of the spatial pattern of the attenuation portion 162 of the second filter 152.

[0075] Data mapping the location of ipRGC in the retina of the eye can be used by device 100 to determine the target area of ​​the retina.

[0076] To be from Figure 6 It is understood that the first filter 151 and the second filter 152 are mirror images of each other, and they have reflective symmetry on a substantially vertical center line that separates the two filters, which spatially corresponds to the approximate center line between the user's first eye 131 and the second eye 132.

[0077] In some examples, filters 151 and 152 are fixed. In other examples, filters 151 and 152 are adapted, as described later. In still other examples, a pair of first filters 151 and second filters 152 may be selected from a library of filter pairs 151 and 152. The selection of a particular filter pair 151 and 152 may, for example, be based on user characteristics. Thus, controller 110 can be configured to select a predefined region of the retina as the target region of the retina of a first eye from multiple different predefined regions of the retina based on user characteristics, the multiple different predefined regions of the retina being associated with different characteristics.

[0078] The arrangement of the attenuation portions 161, 162 in their respective first filters 151 and second filters 152 depends, for example, on the relative positions of the user's eyes 131, 132 and the display 120. The relative positions of the user's eyes 131, 132 determine the relative positions of the target regions of the user's retina 131, 132 associated with the NIF function.

[0079] In some examples, the relative position of the eye / target area and the display is primarily influenced by the user's eyes 131, 132. This could be due to inherent characteristics of the user's eyes or due to eye movements.

[0080] In other examples, the relative position of the eye / target area and the display is affected by the location and / or orientation of the display 120.

[0081] In other examples, the relative positions of the eye / target area and the display depend on the positions of the user's eyes 131, 132 and the display 120.

[0082] In some examples, the relative positions of eyes 131, 132 and the display are considered fixed. In other examples, the relative positions of eyes 131, 132 and the display depend on user characteristics. In other examples, the relative positions of eyes 131, 132 and the display are based on an estimate of relative positioning. In other examples, the relative positions of eyes 131, 132 and the display depend on sensing the relative positions of the user's eyes 131, 132 and / or the relative position of the display 120.

[0083] Relative positioning can depend on the known or estimated ipRGC distribution in the user's retina. Relative positioning can also depend on the known or estimated position of the user's eye and gaze relative to the display 120.

[0084] Therefore, the filter region 11 in the first image 121 can be positioned based on the relative position of the user's first eye 131 relative to the display 120, which displays the first image 121 to the user's first eye 131. Similarly, the filter region 12 in the second image 122 can be positioned based on the relative position of the user's second eye 132 relative to the display 120, which displays the second image 122 to the user's second eye 132.

[0085] Figure 7 The illustration shows an example of how filters 151 and 152 dynamically adapt based on the relative movement of the sensed user's eyes 131 and 132 and the display 120.

[0086] exist Figure 7 In the frame 170, the user's eyes 131 and 132 are tracked.

[0087] The positions of portions of the user's first eye 131 and second eye 132 can be determined based on computer vision analysis of images (or multiple images) captured by a camera with a known position relative to the display 120. The positions can depend on the position of the user's head and the position of the eyes 131, 132 relative to the head (gaze direction). The positions of the eyes 131, 132 can therefore be defined by location and / or orientation.

[0088] In box 172, the movement of display 120 is tracked. The location and orientation of display 120 can be tracked using inertial sensors such as gyroscopes and accelerometers to track the position of device 100 (or display 120).

[0089] In box 174, the relative movement of the first eye 131 relative to the display 120 and the relative movement of the second eye 132 relative to the display 120 are determined. These results are then used in box 176 to adapt the first filter 151 and the second filter 152.

[0090] The algorithm used to determine the position of eyes 131, 132 relative to display 120 determines location and / or orientation for each eye. In addition to processing image data from the camera to track the user's eyes 131, 132 using computer vision, the algorithm may additionally (or alternatively) process device usage data, such as device orientation, grip style, etc.

[0091] If the holographic display 120 is used, the algorithm can be used to calculate the eye angle relative to the display 120, as this is an additional parameter that can be controlled by the holographic display or other light field display.

[0092] In some examples, the adaptation of filters 151, 152 occurs within controller 110. The box 174 determining the relative movement of the eye and display may occur within controller 110 in some examples, but may also occur elsewhere. In some examples, only eye tracking occurs. In other examples, only display tracking occurs. In some examples, eye tracking 170 may be determined at least partially by controller 110. In some examples, the tracking of displays 120, 170 may be determined at least partially by controller 110.

[0093] Figure 8 Another example of the adaptation of filters 151, 152 is illustrated. In this example, in box 180, the exposure of a target region of the retina of the user's eyes 131, 132 associated with the NIF function to blue light is estimated, and therefore, filters 151, 152 are adapted in box 182. In some, but not all, examples, filters 151, 152 are adapted in box 182 to prevent the target region from being exposed to blue light in a manner that could adversely affect the user's biological system regulated by the NIF function, such as, for example, circadian rhythms. For example, in some examples, the attenuation applied by filter 182 is controlled to the extent necessary to prevent adverse biological effects, but nothing more.

[0094] In one example, within box 180, there is a comparison of estimates of blue light intensity at one or more target regions of the retina of a user's eyes 131, 132 associated with the NIF function with a threshold. A first filter 151 and a second filter 152 are adapted to prevent the threshold from being exceeded.

[0095] In some, but not all, examples, the blue light intensity is estimated as an instantaneous intensity of the blue light, and filters 151 and 152 are adjusted to prevent it from exceeding a first threshold.

[0096] In some, but not all, examples, the blue light intensity is estimated as the cumulative intensity of blue light over a given time period, and filters 151 and 152 are adapted to prevent it from exceeding a second threshold.

[0097] Device 100 can be configured to receive user input to manually change multiple thresholds. For example, a user can manually type in a night mode with a specified threshold setting. The threshold setting can be automatic or user-adjustable.

[0098] Device 100 can be configured to automatically change one or more thresholds. For example, this automatic change can be dependent on or responsive to environmental changes and / or user actions. For instance, the thresholds might vary depending on the time of day, decreasing as the expected bedtime approaches. Additionally, the thresholds might depend on ambient light conditions, such as the intensity of ambient blue light.

[0099] Figure 9 The illustration shows an example of filters 151 and 152 adapting based on the user's estimated ipRGC location. The ipRGC location can be selected from the ipRGC location library based on the user's characteristics.

[0100] This database can be a collection of known or estimated locations of relevant ipRGC populations within a user's retina. It can identify retinal segments and the presence or density of ipRGC populations. Given average knowledge of human retinal anatomy (e.g., the finding that ipRGC density is greater in the nasal retina than in the temporal retina), this data can be predefined. Optionally, location data can be refined using demographic or other categorical data.

[0101] The library can also record the NIF function response to exposure in different retinal regions, where the response to exposure can be measured. This allows for different exposure thresholds for different regions and / or different attenuation portions for different regions [161, 162].

[0102] exist Figure 9 In the illustrated example, machine learning engine 190 is used to determine target regions 192 of the retina of the user's eyes 131, 132 associated with the NIF function, and then used in box 194 to adapt filters 151, 152.

[0103] In order to create the machine learning engine 190, the values ​​of the parameters that parameterize the NIF function were measured.

[0104] For example, one NIF function is regulating the release of the hormone melatonin. Melatonin regulates the sleep-wake cycle. Therefore, it can be assessed based on the user's sleep. The user's sleep can be parameterized, for example, by movement and breathing patterns. Movement can be measured using inertial sensors. Breathing patterns can be measured using microphones.

[0105] As previously mentioned, other NIF functions associated with ipRGC include, for example, mediating pupillary light response, regulating appetite, and regulating mood. Eye-tracking systems can be used to monitor pupillary light response. Calorie counting apps or other diet monitoring apps can be used to monitor appetite. Mood can be monitored via facial expression recognition, monitoring of physiological parameters such as heart rate, respiratory rate, and skin conductance, or through direct user input specifying the mood.

[0106] The Machine Learning Engine 190 was created using training data that was formed by pairing the values ​​of parameters that parameterize the NIF function with data indicating the images displayed during a defined time period (e.g., 30 minutes to 1 hour before bedtime) before the parameter values ​​were measured.

[0107] Data indicating the images displayed during a defined time period may include estimated blue light exposure patterns on the retina of the first and / or second eyes 131, 132. The blue light exposure patterns may be estimated based on pre-processed images.

[0108] Forming training data can include labeling the values ​​of parameters that parameterize the NIF function with blue light exposure patterns estimated for images during the period prior to observing these values.

[0109] The machine learning engine 190 can be trained using training data to predict blue light exposure patterns given subsequent parameter values.

[0110] Once the machine learning engine 190 has been trained, parameter values ​​indicating perturbations to the NIF function can be provided as input to the machine learning engine 190. In response, the machine learning engine 190 provides an output blue light exposure pattern. This output blue light exposure pattern from the trained machine learning engine 190 includes an estimate of the blue light exposure pattern predicted to trigger perturbations to the NIF function.

[0111] The estimation of blue light exposure patterns predicted to trigger disturbances to NIF function can be used to identify target areas 192 of the retina of users' eyes 131, 132 associated with NIF function.

[0112] Image preprocessing and training of the machine learning engine 190 can be performed locally at device 100; remotely at one or more devices; or partially locally and partially remotely at one or more devices.

[0113] In other examples, data mapping ipRGC locations in the retina of the first eye 131 and / or the second eye 132 can be received at controller 110. For example, the data mapping ipRGC locations can be obtained by an optician who can provide the data to the user for loading into controller 110. Target region 192 can be defined based on these ipRGC locations, for example, to include these locations.

[0114] As previously described, filters 151 and 152 can be dynamically adapted. However, it may also be desirable to control the extent to which adaptation occurs.

[0115] Figure 10The illustration shows an example of filter self-adaptation being constrained. In this example, the constraints defined in box 200 are used to control or constrain the adaptation of filters 151 and 152 in box 202.

[0116] In at least some examples, the constraints prevent or reduce the likelihood that a user will notice or be stimulated by the filtering of image 102 by the first filter 151 and the second filter 152.

[0117] In some, but not all, examples, the reduction of the blue spectral component of the visual content in filter region 11 of the first image 121 is controlled to prevent the reduction from exceeding an image quality threshold.

[0118] In some, but not all, examples, the reduction of the blue spectral component of the visual content in the filter region 11 of the first image 121 is controlled to prevent the spatial contrast between the filter region 11 of the first image 121 and its adjacent unfiltered regions from exceeding different image quality thresholds. For example, the fading or fading gradient of the attenuation portions 161, 162 in filters 151, 152 can be controlled.

[0119] In some, but not all, examples, the reduction of the blue spectral component of the visual content in filter region 11 of the first image 121 is controlled based on one or more aspects that can affect user perception, such as, for example, media type, gaze location, and ambient light level.

[0120] Constraints can also be adapted based on the user’s response to the applied filters 151 and 152.

[0121] It is important to understand that at least some of the aforementioned examples are able to control the exposure of the ipRGC population to the retina with minimal or no negative impact on the aesthetic or functional characteristics of the image perceived by the user.

[0122] Figure 11 An example of a holographic display 120 for binocular display of visual content is illustrated.

[0123] The holographic display 120 includes a diffraction element array 220. The diffraction element array 220 is configured to direct the backlight 210 toward at least a first set of principal directions 221 toward a first eye 131 of the user and a second set of different principal directions 222 toward a second eye 132 of the user.

[0124] The holographic display 120 also includes at least a first pixel array 231 and a second distinct pixel array 232. The first pixel 231 is configured to display a first image 121. The second pixel 232 is configured to display a second image 122.

[0125] The first pixel array 231 is arranged relative to the diffraction element 220 such that the backlight 210 guided into the first principal direction set 221 illuminates the first pixel 231. The second pixel array 232 is arranged relative to the diffraction element 220 such that the backlight 210 guided into the second principal direction set 222 illuminates the second pixel 232.

[0126] Since the positions of the user's eyes 131 and 132 can be changed relative to the holographic display 120, the holographic display 120 can be controlled depending on the positions of the user's eyes 131 and 132 to point the first image 121 to the first eye 131 and the second image 122 to the second eye 132.

[0127] In some examples, the diffractive element array 220 is configured to direct the backlight 210 to more than two sets of principal directions. These sets of principal directions may be fixed. A first set of principal directions 221 facing the user's first eye 131 may be selected from the fixed set of principal directions based on the position of the user's first eye 131 relative to the holographic display 120. A set of principal directions 222 facing the user's second eye 132 may be selected from the fixed set of principal directions based on the position of the user's second eye 132 relative to the holographic display 120.

[0128] The first pixel array 231 and the second pixel array 232 can form part of the larger pixel array 230.

[0129] The individual pixels of the larger array 230, which are identified as the first pixel 231, are determined based on the position of the user's first eye 131 relative to the holographic display 120. The first pixel 231 includes the pixels of the larger array 230, which is arranged relative to the diffraction element 220 such that they are illuminated by the backlight 210 of the selected first principal direction set 221.

[0130] The individual pixels of the larger array 230, which are identified as the second pixel 232, are determined based on the position of the user's second eye 132 relative to the holographic display 120. The second pixel 232 includes the pixels of the larger array 230, which is arranged relative to the diffraction element 220 such that they are illuminated by the backlight 210 of the selected second principal direction set 222.

[0131] Figure 12 An example of method 300 is illustrated. In box 310, method 300 includes generating a first image of visual content. In box 320, method 300 includes generating a second image of visual content. In box 330, method 300 includes binocular display of visual content as a first image pointing to a user's first eye and a second image pointing to a user's second eye.

[0132] The first image includes a first region in which, compared to the corresponding first region of the second image, high-frequency spectral components of the visual content (such as blue spectral components) are reduced.

[0133] The second image includes a second distinct region in which, compared to the corresponding second region of the first image, the high-frequency spectral components of the visual content (such as the blue spectral component) are reduced.

[0134] Figure 13 An example of controller 110 is illustrated. Controller 110 can be implemented as a controller circuit system. Controller 110 can be implemented solely in hardware, have certain aspects in software that includes firmware, or be a combination of hardware and software (including firmware).

[0135] like Figure 13 As illustrated, the controller 110 can be implemented using instructions that enable hardware functionality, such as executable instructions of a computer program 113 that can be stored in a computer-readable storage medium (disk, memory, etc.) for execution by such a processor 111.

[0136] Processor 111 is configured to read from and write to memory 112. Processor 111 may also include: an output interface through which data and / or commands are output; and an input interface through which data and / or commands are input to processor 111.

[0137] Memory 112 stores a computer program 113 comprising computer program instructions (computer program code) that, when loaded into processor 111, control the operation of device 100. The computer program instructions of computer program 113 enable the device to execute... Figure 3 , 7 The logic and routines of the methods, blocks, or functions illustrated in Figures 10 and 12. By reading memory 112, processor 111 is able to load and execute computer program 113.

[0138] Therefore, device 100 includes:

[0139] At least one processor 111; and

[0140] At least one memory 112, including computer program code

[0141] At least one memory 112 and computer program code are configured, together with at least one processor 111, to cause the device 100 to perform at least the following:

[0142] Controlling the binocular display of visual content and / or displaying visual content binocularly as a first image pointing to the user's first eye and a second image pointing to the user's second eye.

[0143] The first image includes a first region, in which the blue spectral component of the visual content is reduced compared to the corresponding first region of the second image, and

[0144] The second image includes a second distinct region in which the blue spectral component of the visual content is reduced compared to the corresponding second region of the first image.

[0145] like Figure 14 As illustrated, computer program 113 can reach device 100 via any suitable delivery mechanism 400. Delivery mechanism 400 can be, for example, a machine-readable medium, a computer-readable medium, a non-transient computer-readable storage medium, a computer program product, a memory device, a recording medium such as an optical disc read-only memory (CD-ROM) or a digital versatile disc (DVD) or solid-state storage, or an article of manufacture that includes or tangibly implements computer program 113. The delivery mechanism can be a signal configured to reliably transmit computer program 113. Device 100 can propagate or transmit computer program 113 as a computer data signal.

[0146] Computer program instructions are used to cause the device to perform at least the following operations or to perform at least the following operations:

[0147] This causes the visual content to be displayed in binoculars, as a first image pointing to the user's first eye and a second image pointing to the user's second eye.

[0148] The first image includes a first region, in which the blue spectral component of the visual content is reduced compared to the corresponding first region of the second image, and

[0149] The second image includes a second distinct region in which the blue spectral component of the visual content is reduced compared to the corresponding second region of the first image.

[0150] In some examples, the binocular display that causes the above visual content includes a binocular display of the visual content that controls the image, as a first image pointing to the user's first eye and a second image pointing to the user's second eye.

[0151] Computer program instructions can be included in a computer program, a non-transitory computer-readable medium, a computer program product, or a machine-readable medium. In some, but not all, examples, computer program instructions can be distributed across more than one computer program.

[0152] Although memory 112 is illustrated as a single component / circuit system, it can be implemented as one or more separate components / circuit systems, some or all of which may be integrated / removable and / or provide permanent / semi-permanent / dynamic / cached storage devices.

[0153] Although processor 111 is illustrated as a single component / circuit system, it can be implemented as one or more separate component / circuit systems, some or all of which may be integrated / removable. Processor 111 can be a single-core or multi-core processor.

[0154] References to “computer-readable storage medium,” “computer program product,” “tangibly implemented computer program,” or “controller,” “computer,” “processor,” etc., should be understood to encompass not only computers with different architectures (such as single / multiprocessor architectures and sequential (von Neumann) / parallel architectures) but also special-purpose circuits, such as field-programmable gate arrays (FPGAs), special-purpose circuits (ASICs), signal processing devices, and other processing circuitry systems. References to computer programs, instructions, code, etc., should be understood to encompass software used in programmable processors or firmware, such as the programmable content of hardware devices, whether instructions for processors or configuration settings for fixed-function devices, gate arrays, or programmable logic devices.

[0155] As used in this application, the term 'circuit system' may refer to one or more of the following:

[0156] (a) Implementations only in hardware circuit systems (such as implementations only in analog and / or digital circuit systems); and

[0157] (b) A combination of hardware circuitry and software, such as (if applicable):

[0158] (i) a combination of (multiple) analog and / or digital hardware circuits and software / firmware, and (ii) any part of (multiple) hardware processors having software (including (multiple) digital signal processors), software, and (multiple) memories, which work together to enable a device such as a mobile phone or server to perform various functions, and

[0159] (c) (Multiple) hardware circuits and / or (multiple) processors, such as (multiple) microprocessors or a portion thereof, require software (e.g., firmware) for operation, but may be absent when operation is not required.

[0160] This definition of circuit system applies to all uses of the term in this application, including in any claim. As yet another example, as used herein, the term circuit system will also cover only the implementation of hardware circuitry or processors and their accompanying software and / or firmware. For example, and if applicable to a particular claim element, the term circuit system will also cover baseband integrated circuits for mobile devices or similar integrated circuits used in servers, cellular network devices, or other computing or networking devices.

[0161] Figure 3 , Figures 7 to 10 , Figure 12 The boxes illustrated may represent steps in the method and / or code segments in computer program 113. The illustration of a specific order of boxes does not necessarily imply that the boxes have a desired or preferred order, and the order and arrangement of the boxes can be changed. Furthermore, some boxes may be omitted.

[0162] Figure 1 The device 100 may be or may include Figure 13 The controller 110, or may be or include a controller capable of receiving signals from... Figure 14 The delivery agency 400 reads the computer program 113 and runs the computer program 113 on any computer or machine.

[0163] It should be understood that device 100 may include any suitable components for performing the functions described above.

[0164] Therefore, in some examples, device 100 includes components for the following operations:

[0165] Controlling the binocular display of visual content and / or displaying visual content binocularly as a first image pointing to the user's first eye and a second image pointing to the user's second eye.

[0166] The first image includes a first region, in which the blue spectral component of the visual content is reduced compared to the corresponding first region of the second image, and

[0167] The second image includes a second distinct region in which the blue spectral component of the visual content is reduced compared to the corresponding second region of the first image.

[0168] Where a structural feature has been described, it can be replaced by a component that performs one or more functions of the structural feature, whether those functions are described explicitly or implicitly.

[0169] Systems, apparatuses, methods, and computer programs can use machine learning, which may include statistical learning. Machine learning is a field of computer science that enables computers to learn without explicit programming. If a computer's performance (measured by P) at a task in T improves with experience E, then it learns from experience E relative to a class of tasks T and the performance metric P. Computers can typically learn from previous training data to predict future data. Machine learning includes fully or partially supervised learning and fully or partially unsupervised learning. It can enable discrete outputs (e.g., classification, clustering) and continuous outputs (e.g., regression). For example, machine learning can be implemented using different methods, such as cost function minimization, artificial neural networks, support vector machines, and Bayesian networks. For example, cost function minimization can be used for linear and multinomial regression and K-means clustering. Artificial neural networks (e.g., with one or more hidden layers) model complex relationships between input and output vectors. Support vector machines can be used for supervised learning. Bayesian networks are directed acyclic graphs that represent the conditional independence of multiple random variables.

[0170] The algorithm described above can be applied to achieve the following technical effects: reduce the disturbance to the expected color palette of visual content, while reducing the exposure of ipRGC to blue light.

[0171] The example above finds the application as an enabled component for the following:

[0172] Automotive systems; telecommunications systems; electronic systems, including consumer electronics; distributed computing systems; media systems for generating or rendering media content, including audio, visual, and audiovisual content, as well as mixed, mediated, virtual, and / or augmented reality; personal systems, including personal health systems or personal fitness systems; navigation systems; user interfaces, also known as human-computer interfaces; networks, including cellular, non-cellular, and optical networks; temporary networks; the Internet of Things; the Internet of Things; virtualized networks; and related software and services.

[0173] The term "includes" is used in this document in an inclusive rather than exclusive sense. That is, any reference to X that includes Y indicates that X may include only one Y or may include more than one Y. If the intention is to use "includes" with an exclusive meaning, it will be obvious in the context by referring to "includes only one..." or by using "consisting of...".

[0174] Various examples have been referenced in this description. Descriptions of features or functions of an example indicate which features or functions exist in that example. Whether explicitly stated or not, the terms 'example,' 'for example,' 'can,' or 'may' are used herein to indicate that such a feature or function exists at least in the described example, whether or not it is described as an example, and that they may, but not necessarily, exist in some or all other examples. Thus, 'example,' 'for example,' 'can,' or 'may' refer to a specific instance of a class of examples. The characteristics of an instance can be characteristics of only that instance, or characteristics of this class, or characteristics of subclasses of that class, including some but not all instances of that class. Therefore, it is implicitly disclosed that features described with reference to one example rather than another may, where possible, be used in that other example as part of a work composition, without necessarily being used in that other example.

[0175] Although examples have been described with reference to various examples in the preceding paragraphs, it should be understood that modifications to the examples given may be made without departing from the scope of the claims.

[0176] In addition to the combinations explicitly described above, the features described above can be used in combination.

[0177] Although functions are described with reference to certain features, those functions can be performed by other features, whether or not they are described.

[0178] Although features have been described with reference to some examples, those features may also exist in other examples, whether or not they are described.

[0179] The terms “a” or “the” are used in this document in an inclusive rather than exclusive sense. That is, any reference to X that includes one / the Y indicates that X may include only one Y or may include more than one Y, unless the context clearly indicates otherwise. If the intention is to use “a” or “the” with an exclusive meaning, it will become obvious in the context. In some cases, the use of “at least one” or “one or more” may be used to emphasize the inclusive meaning, but the absence of these terms should not be taken as an inference of any exclusive meaning.

[0180] The presence of a feature (or combination of features) in a claim is a reference to that feature or combination of features itself, as well as features that achieve substantially the same technical effect (equivalent features). Equivalent features include, for example, features that are variations and achieve substantially the same result in substantially the same manner. Equivalent features include, for example, features that perform substantially the same function in substantially the same manner to achieve substantially the same result.

[0181] In this description, various examples using adjectives or adjective phrases have been referenced to describe the characteristics of the examples. This description of the characteristics of the examples indicates that in some examples the characteristics are exactly the same as those described, and in others they are substantially the same as those described.

[0182] While efforts have been made to draw attention in the foregoing description to those features deemed particularly important, it should be understood that the applicant may seek protection by means of the claims for any patentable feature or combination of features referenced above and / or shown in the drawings, whether or not this is emphasized.

Claims

1. A device for reducing blue light, comprising: At least one processor; as well as At least one memory, including computer program code, The at least one memory stores instructions, which, when executed by the at least one processor, cause the device to at least: Visual content is displayed binocularly, as a first image pointing to the user's first eye and a second image pointing to the user's second eye. The first image includes a first region in which the blue spectral component of the visual content is reduced compared to the corresponding first region in the second image. The second image includes a second region in which the blue spectral component of the visual content is reduced compared to the corresponding second region of the first image. The first region of the first image and the second region of the second image are determined based on mirror filters applied to the first image and the second image.

2. The apparatus of claim 1, wherein the at least one memory and the instructions stored in the at least one memory are configured, together with the at least one processor, to further cause the apparatus to: apply a first spatial discontinuity spectral filter to form the first image, and apply a different second spatial discontinuity spectral filter to form the second image.

3. The apparatus of claim 2, wherein the first spatial discontinuous spectral filter and the second spatial discontinuous spectral filter are mirror images of each other.

4. The apparatus of claim 1, wherein the first region of the first image is based on a target region of the retina of the first eye, wherein the target region is associated with a non-image forming NIF function.

5. The apparatus of claim 4, wherein the at least one memory and the instructions stored in the at least one memory are configured, together with the at least one processor, to further cause the apparatus to: select a region of the retina from a plurality of different regions of the retina as the target region of the retina of the first eye, based on the characteristics of the user, wherein the plurality of different regions of the retina are associated with corresponding characteristics.

6. The apparatus of claim 4, wherein the at least one memory and the instructions stored in the at least one memory are configured, together with the at least one processor, to further cause the apparatus to: receive data mapping the locations of intrinsically photosensitive retinal ganglion cells (ipRGCs) in the retina of the first eye; and, based on the data, determine the target region of the retina of the first eye.

7. The apparatus of claim 4, wherein the at least one memory and the instructions stored in the at least one memory are configured, together with the at least one processor, to further cause the apparatus to: Measure the values ​​of the parameters that parameterize the non-image forming NIF function; Training data is formed by pairing the values ​​of the parameters with data indicating images to create a machine learning engine, wherein the images are displayed during a time period prior to the measurement of the values ​​of the parameters; and The target region of the retina of the first eye, associated with the non-image-forming NIF function, is obtained from the machine learning engine.

8. The apparatus of claim 1, wherein the at least one memory and the instructions stored in the at least one memory are configured, together with the at least one processor, to further cause the apparatus to: determine one or more characteristics of the first region of the first image based on the position of the user's first eye relative to a display configured for binocular display of the visual content.

9. The apparatus of claim 8, wherein the at least one memory and the instructions stored in the at least one memory are configured, together with the at least one processor, to further cause the apparatus to: determine the position of the user's first eye based on analysis of an image captured by a camera having a known position relative to the display.

10. The apparatus of claim 1, wherein the at least one memory and the instructions stored in the at least one memory are configured, together with the at least one processor, to further cause the apparatus to: control the reduction of the blue spectral component of the visual content in the first region of the first image to prevent one or more of the following: The instantaneous intensity of blue light exceeds the first threshold; or The cumulative intensity of blue light over a given period of time exceeds the second threshold.

11. The apparatus of claim 10, wherein the at least one memory and the instructions stored in the at least one memory are configured, together with the at least one processor, to further cause the apparatus to: change at least one of the first threshold or the second threshold in response to receiving user input.

12. The apparatus of claim 10, wherein the at least one memory and the instructions stored in the at least one memory are configured, together with the at least one processor, to further cause the apparatus to: change at least one of the first threshold or the second threshold in response to one or more changes in environmental conditions.

13. The apparatus of claim 10, wherein the at least one memory and the instructions stored in the at least one memory are configured, together with the at least one processor, to further cause the apparatus to: control the reduction of the blue spectral component of the visual content in the first region of the first image to prevent one or more of the following: The reduction exceeds the third threshold; or The spatial contrast between the first region of the first image and the adjacent regions of the first image exceeds a fourth threshold.

14. The apparatus according to any one of claims 1 to 13, further comprising a display, the display including at least one display screen, wherein the display is configured to display the visual content in a binocular manner.

15. A method for reducing blue light, comprising: Visual content is displayed binocularly, as a first image pointing to the user's first eye and a second image pointing to the user's second eye. The first image includes a first region in which the blue spectral component of the visual content is reduced compared to the corresponding first region in the second image. The second image includes a second region in which the blue spectral component of the visual content is reduced compared to the corresponding second region of the first image. The first region of the first image and the second region of the second image are determined based on mirror filters applied to the first image and the second image.

16. The method of claim 15, wherein the first region of the first image is based on a target region of the retina of the first eye associated with a non-image-forming NIF function.

17. The method of claim 16, further comprising: Based on the characteristics of the user, a region of the retina is selected from multiple different regions of the retina as the target region of the retina of the first eye, wherein the multiple different regions of the retina are associated with corresponding characteristics.

18. The method according to any one of claims 15 to 17, further comprising: Measure the values ​​of the parameters that parameterize the non-image forming NIF function; Training data is formed by pairing the value of the parameter with data indicating the image to create a machine learning engine, wherein the image is displayed during a time period prior to the measurement of the value of the parameter; as well as The target region of the retina of the first eye, associated with the non-image-forming NIF function, is obtained from the machine learning engine.

19. A non-transient computer-readable medium comprising program instructions stored on the non-transient computer-readable medium, causing a device to perform at least the following operations: Visual content is displayed binocularly, as a first image pointing to the user's first eye and a second image pointing to the user's second eye. The first image includes a first region in which the blue spectral component of the visual content is reduced compared to the corresponding first region in the second image. The second image includes a second region in which the blue spectral component of the visual content is reduced compared to the corresponding second region of the first image. The first region of the first image and the second region of the second image are determined based on mirror filters applied to the first image and the second image.

20. The non-transient computer-readable medium of claim 19, wherein the first region of the first image is based on a target region of the retina of the first eye associated with a non-image-forming NIF function.

Images