Projector brightness adjusting method

Abstract

The invention relates to the technical field of projectors, in particular to a projector brightness adjusting method, which comprises the following steps: acquiring reflectivity distribution data of the surface of a projection area in real time, and calculating a local reflectivity gradient; performing non-uniform partitioning on the projection area according to the amplitude of the local reflectivity gradient; for each partition, calculating compensation brightness based on the reflectivity distribution data and the target brightness; and driving the projection light source to output according to the compensation brightness, and updating the reflectivity distribution data in real time. The method and the device have the effect of solving the problem of non-uniform brightness of a picture caused by the change of the reflectivity.

Description

Technical Field

[0001] The present application relates to the technical field of projectors, and in particular to a method for adjusting the brightness of a projector. Background Art

[0002] The popularity and innovation of projection technology are constantly expanding its application scenarios, extending from traditional conference rooms and classrooms to home theaters, outdoor displays and even mobile business fields. Users' requirements for projection image quality are no longer limited to resolution and color performance, but also focus on brightness consistency, energy efficiency and dynamic adaptability in complex environments. Although the existing automatic brightness adjustment technology has made progress in ambient light perception and content analysis, its core framework still has significant limitations, especially in terms of adaptability to non-ideal projection surfaces. It has long faced technical bottlenecks, which has restricted the further improvement of user experience.

[0003] Mainstream brightness adjustment solutions mainly rely on two technical paths: ambient light sensing and image content analysis. Ambient light feedback control uses photoelectric sensors to collect ambient light illumination in real time, and combines closed-loop algorithms to adjust the output power of the light source. This type of method assumes that the ambient light is evenly distributed and the projection surface is an ideal diffuse reflector, but in fact it is unable to perceive the spatial differences in the surface reflection characteristics. For example, dark walls will greatly attenuate the reflected light intensity, causing the sensor to misjudge that the ambient light is sufficient and reduce the brightness, but the actual picture is dim and blurred due to insufficient reflectivity. What's more serious is that the sensor is easily interfered by strong light sources outside the projection area, and the measurement deviation is particularly significant on mirrors or semi-transparent surfaces (such as glass), causing compensation failure.

[0004] Image content adaptive adjustment dynamically adjusts output parameters by analyzing the brightness histogram of the video signal, attempting to enhance details in dark scenes or suppress overexposure in bright scenes. However, this type of method only processes the original signal and does not incorporate the impact of surface reflectivity on the final display effect into the calculation model. When the same high-brightness image is projected onto a white wall and a gray wall, the actual difference in perception can reach more than 30%, but traditional algorithms are powerless to do anything about this. What's worse, the strategy of globally increasing brightness to compensate for low-reflectivity surfaces often leads to overexposure of high-reflectivity areas, resulting in loss of highlight details and compression of the dynamic range.

[0005] Although the preset mode and manual calibration functions can serve as supplementary solutions, allowing users to select fixed scene modes or optimize parameters through a colorimeter. Although such methods can improve the experience in static environments, they are difficult to cope with dynamic changes in surface reflectivity. In emerging scenarios such as smart dimming glass and mobile projection screens, the reflectivity distribution may change several times per second, and static calibration parameters quickly become invalid. Ordinary users not only have a fragmented experience when faced with frequent manual adjustments, but also find it difficult to achieve precise optimization due to lack of professional knowledge.

[0006] Projection display is essentially a three-element system of light-surface-human eye. The surface reflectivity is a key intermediary variable that directly determines the final image quality. The theoretical assumption of an ideal diffuse reflector frequently fails in practical applications: the concave-convex structure of textured wallpaper leads to a disordered distribution of incident angles, the selective reflection of warm-colored paint on specific wavelengths of light causes color deviation, and the glare area produced by mirror materials destroys the uniformity of the picture. The addition of dynamic surface characteristics further increases the complexity: the reflectivity of smart glass can change instantaneously with electrical signals, and the projection area on the surface of the rotating booth continuously shifts. The response delay of the traditional technical framework to such scenes can reach hundreds of milliseconds, resulting in flicker and smear visible to the naked eye.

[0007] The coupling effect of ambient light and surface reflection is often underestimated. When the ambient light intensity is close to that of the projected light, a slight change in reflectivity will significantly change the contrast of the picture. Taking gray walls and white walls as examples, the former has a low reflectivity, which makes the projected light dominate, while the latter has a high reflectivity, which increases the reflection of ambient light, making dark scenes white and reducing color saturation. The existing algorithm does not establish a dynamic model of ambient light, projected light, and surface reflection. It only adjusts the output through simple addition and subtraction operations, which makes it difficult to achieve physically accurate brightness compensation.

[0008] In recent years, academia and industry have tried to introduce surface reflectivity compensation, but mature solutions are still to be developed. Methods based on pre-stored surface databases rely on users to manually input material types and cannot adapt to local differences on the same surface; camera-assisted measurement solutions are severely affected by ambient light and require too much computing power for real-time processing; dual-light source detection technology requires dedicated hardware support, and cost and power consumption restrict commercialization. These attempts have not solved the core problem due to insufficient accuracy or poor real-time performance.

[0009] The evolution of market demand is driving a paradigm shift in technology. The popularity of IoT devices and the emergence of smart materials are making projection scenes increasingly complex and diverse. Users are in urgent need of a solution to the above problems to break through the path dependence of traditional ambient light and content analysis. Summary of the invention

[0010] In order to solve the above problems, the present application provides a method for adjusting the brightness of a projector.

[0011] The present application provides a method for adjusting the brightness of a projector, which adopts the following technical solution: A method for adjusting the brightness of a projector comprises the following steps: Obtain the reflectivity distribution data of the projection area surface in real time and calculate the local reflectivity gradient; The projection area is non-uniformly partitioned according to the magnitude of the local reflectivity gradient; For each partition, the compensation brightness is calculated based on the reflectivity distribution data and the target brightness; The projection light source output is driven according to the compensation brightness, and the reflectivity distribution data is updated in real time.

[0012] In one embodiment, the step of acquiring the reflectivity distribution data of the surface of the projection area in real time and calculating the local reflectivity gradient specifically includes: Based on the acquisition position of the projection area, the reflected light intensity, the test light intensity and the surface normal inclination angle are obtained, and the calculation formula of the reflectivity distribution data at the position (x, y) is: ; in, is the reflected light intensity, To test the light intensity, is the surface normal inclination, k is the sensor calibration coefficient; The calculation formula of the local reflectivity gradient at position (x, y) is: ; In the formula, and The discretized reflectivity matrix ρ[i][j] is obtained by calculating the partial derivative using the central difference method. The formula is: ; ; Where Δx, Δy are the sensor pixel pitches.

[0013] In one embodiment, the step of non-uniformly partitioning the projection area according to the amplitude of the local reflectivity gradient specifically includes: Divide the projection area into initial large partitions according to preset specifications; Comparing the local reflectivity gradient of the initial large partition with a preset gradient threshold, if it is greater than the gradient threshold, splitting the initial large partition into multiple sub-partitions, and recalculating the local reflectivity gradient of each sub-partition; After each split, the local reflectivity gradient of each sub-partition is re-compared with the preset gradient threshold until all partitions pass the judgment; wherein, passing the judgment means that the local reflectivity gradient is greater than the gradient threshold, or the sub-partition size reaches the minimum preset size.

[0014] In one embodiment, the step of non-uniformly partitioning the projection area according to the amplitude of the local reflectivity gradient further comprises: Traverse all partitions to detect whether adjacent partitions meet the merging condition; the merging condition is that the local reflectivity gradient is less than or equal to half of the gradient threshold, and the difference in reflectivity mean is less than the preset difference; Merge the adjacent partitions that meet the conditions into superpixel partitions; A morphological erosion operation is performed on the merged partition boundaries to eliminate jagged edges.

[0015] In one embodiment, the calculation formula of the compensation brightness is: ; In the formula, is the target brightness, is the local reflectivity, is the global average reflectivity, is the contrast adaptive weight; The local reflectivity refers to the average reflectivity of the partition.

[0016] In one embodiment, when the partition surface is inclined, the calculation formula of the local reflectivity is: ; In the formula, is the physical area of ​​the ith partition; When there is no inclination on the partition surface, the calculation formula of the local reflectivity is: ; In the formula, The partition area.

[0017] In one embodiment, the contrast adaptive weight is adjusted by: When the dynamic range of the image content is greater than the preset value, the calculation formula of the contrast adaptive weight is: ; In the formula, is the dynamic range; When fast switching of the screen is detected, the contrast adaptive weight decays exponentially, and its calculation formula is: ; Where, τ is the decay time constant; The calculation formula of dynamic range is: ; In the formula, is the highest brightness value of all partitions in the entire projection area, It is the minimum brightness value of all partitions in the entire projection area.

[0018] In one of the embodiments: the step of detecting fast switching of the picture is: if the inter-frame difference method detects the fast switching, and at least one of the optical flow speed or the scene cutting detects the fast switching.

[0019] In one embodiment, the following exception handling steps are also included: If the local reflectivity is less than the preset reflectivity threshold, it is determined as an uncompensable area, marked as a shadow area and the brightness of the area is reduced.

[0020] In one of the embodiments: the brightness of the subarea marked as the shadow area is adjusted to 0.3 times the target brightness. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 Schematic diagram of the steps of the projector brightness adjustment method of this embodiment. DETAILED DESCRIPTION

[0022] The present application is further described in detail below in conjunction with the accompanying drawings.

[0023] A method for adjusting the brightness of a projector, such as Figure 1 As shown, the following steps are included: S200, obtaining reflectivity distribution data of the surface of the projection area in real time, and calculating the local reflectivity gradient.

[0024] Among them, the reflectivity distribution data is obtained through a multimodal sensor, which includes a visible light camera, a time-of-flight (ToF) sensor, and an active light source array. The visible light camera captures the color distribution (such as whether the wall is warm-toned) and texture features (such as wood grain and tile joints) of the projection surface through the RGB channel, analyzes the brightness distribution after the projection light is reflected, and generates a reflected light intensity map. The time-of-flight (ToF) sensor measures the distance d(x, y) of each point on the projection surface, generates a depth map, and calculates the surface normal direction θ(x, y) through the depth difference of adjacent points. The active light source array emits a test light spot of known intensity, and the measured reflected light intensity is obtained after being measured by the visible light camera.

[0025] This step specifically includes: Based on the acquisition position of the projection area, the reflected light intensity, the test light intensity and the surface normal inclination angle are obtained. The coordinates of the acquisition position are represented by (x, y), where the calculation formula for the reflectivity distribution data at the position (x, y) is: ; in, is the reflected light intensity, To test the light intensity, is the surface normal inclination, and k is the sensor calibration coefficient. Represents the reflectivity of any position (x, y) on the projection surface. It is a continuous function with a value range of [0,1], where 0 represents total absorption and 1 represents total reflection.

[0026] The local reflectivity gradient at position (x, y) indicates the maximum rate of change of reflectivity at position (x, y) on the projection surface. The unit is reflectivity unit / distance unit (such as % / mm). The larger the gradient amplitude, the more significant the difference in reflectivity between adjacent areas (such as black spots on white walls, mirror reflection points, wall bumps, cracks or edges of decorative objects, etc.). The calculation formula is: ; In the formula, and The discretized reflectivity matrix ρ[i][j] is obtained by calculating the partial derivative using the central difference method. The formula is: ; ; Where Δx, Δy are the sensor pixel pitches.

[0027] S400, performing non-uniform partitioning on the projection area according to the amplitude of the local reflectivity gradient.

[0028] Specifically, the projection area is partitioned by the local reflectivity gradient, and the low-gradient area is enlarged and the high-gradient area is reduced by segmentation and merging. The high-gradient area is determined as the reflectivity mutation area (such as wall cracks and material boundaries). By reducing the high-gradient area, the adjustment accuracy is improved and millimeter-level compensation is achieved. By increasing the low-gradient area, the output frequency of the light source is reduced, the calculation load is reduced, and power consumption is saved.

[0029] This step specifically includes: The projection area is divided into initial large partitions according to preset specifications. The initial large partitions are set according to the area of ​​the projection area, and are usually set to 50mm×50mm to reduce the calculation load.

[0030] Compare the local reflectivity gradient of the initial large partition with the preset gradient threshold. If it is greater than the gradient threshold, the initial large partition is split into multiple sub-partitions, and the local reflectivity gradient of each sub-partition is recalculated. The number of sub-partitions formed after each split is n. 2 , n is a natural number.

[0031] After each split, the local reflectivity gradient of each sub-partition is re-compared with the preset gradient threshold until all sub-partitions pass the judgment. Passing the judgment means that the local reflectivity gradient is greater than the gradient threshold, or the sub-partition size reaches the minimum preset size. The purpose of setting the minimum preset size is to control the calculation load, which is usually set to 2mm*2mm.

[0032] After all partitions pass the judgment, all partitions are traversed to detect whether adjacent partitions meet the merging conditions. The merging conditions are that the local reflectivity gradients are less than or equal to half of the gradient threshold, and the difference in reflectivity means is less than the preset difference.

[0033] The qualified adjacent partitions are merged into superpixel partitions. The size of the superpixel partition is usually not larger than the initial large partition. However, when the processor load exceeds 80%, emergency merging can be performed. The superpixel partition formed after forced merging of low-gradient areas can be larger than the initial large partition.

[0034] A morphological erosion operation is performed on the merged partition boundaries to eliminate jagged edges.

[0035] S600 : For each partition, calculate compensation brightness based on reflectivity distribution data and target brightness.

[0036] The calculation formula for compensation brightness is: ; In the formula, is the target brightness, is the local reflectivity, is the global average reflectivity, is the contrast adaptive weight.

[0037] Among them, the local reflectivity refers to the average reflectivity of the partition. Through dynamic partitioning, the continuous reflectivity field Discretize into a finite number of local values , each Represents the reflection characteristics of the corresponding partition.

[0038] The local reflectivity can be calculated by the following two formulas. In this implementation, the following two formulas are used to calculate the local reflectivity according to the different surface conditions of the partitions: When the partition surface is tilted, that is, the actual surface contains tilt, bumps, etc., the local reflectivity accuracy depends on the surface normal inclination angle. , so the local reflectivity is based on the surface normal inclination angle provided by the ToF sensor ,right The cosine correction weighting is obtained, and the calculation formula is: ; In the formula, is the physical area of ​​the i-th partition.

[0039] When there is no tilt on the partition surface, that is, the partition is located on an ideal flat surface, the calculation formula for the local reflectivity is: ; In the formula, is the partition area (unit: mm2), Indicates the physical distance of the partition surface in the x / y direction.

[0040] Among them, through the above dynamic partitioning, the high gradient area It is closer to the real local value and suppresses the interference of adjacent areas. In addition, it should be noted that the use of different calculation formulas according to whether the partition surface is tilted is mainly to reduce the calculation load while ensuring the calculation accuracy. If the processor load is sufficient, only the calculation formula after cosine correction weighting can be used. Otherwise, only the second calculation formula can be used.

[0041] In this embodiment, the contrast adaptive weight is adjusted as follows: When the dynamic range of the image content is greater than the preset value, the calculation formula of the contrast adaptive weight is: ; In the formula, is the dynamic range. The calculation formula of the dynamic range is: ; In the formula, is the highest brightness value of all partitions in the entire projection area, It is the minimum brightness value of all partitions in the entire projection area.

[0042] When fast switching of the screen is detected, the contrast adaptive weight decays exponentially, and its calculation formula is: ; Where τ is the decay time constant.

[0043] The step of detecting a fast switching of the picture is as follows: if the fast switching is detected by the inter-frame difference method, and the fast switching is detected by at least one of the optical flow speed or the scene cutting, the compensation weight is reduced during the fast switching by means of the multi-modal decision logic to avoid brightness flickering caused by reflectivity update delay.

[0044] The technical standard for detecting fast switching of images is that the content difference between adjacent frames exceeds a threshold, which is usually manifested as: 1. The brightness histogram changes dramatically (such as the overall brightness changes by more than 50%); 2. The motion vector increases suddenly (such as the optical flow speed exceeds 30 pixels / frame); 3. Scene cut detection (such as edge feature matching degree drops by more than 70%).

[0045] The inter-frame difference method detects the sudden change of overall brightness, and its specific steps include: Luminance channel extraction: Convert the input video frame into a grayscale image 。

[0046] Differential calculation: Calculate the difference between adjacent frames 。

[0047] Normalization processing: , where is the resolution.

[0048] Threshold determination: If > τ, it is determined as a fast switch.

[0049] The specific steps of optical flow motion analysis include: Sparse optical flow calculation: Use the Lucas-Kanade algorithm to track the motion speed of feature points 。

[0050] Average speed calculation, the calculation formula is: ; Motion sudden increase determination: If , it is confirmed as a fast switch, where is a preset value, such as 20 pixels / frame, etc.

[0051] The steps of scene cut detection specifically include: Edge feature extraction: Use the Canny operator to detect the edge map E of the current frame t 。

[0052] Feature matching degree calculation, the calculation formula is: ; In the formula, represents the number of edge pixels co-existing in two frames, and its calculation method is pixel-by-pixel comparison. If both positions of the two frames are 1, the result is 1, otherwise it is 0; represents the sum of all edge pixels in two frames, and its calculation method is pixel-by-pixel comparison. If at least one of the two frames at the same position is 1, the result is 1, otherwise it is 0.

[0053] Scene cut determination: If < m, it is determined as a scene switch, where the value of m is less than 1.

[0054] In addition, in one of the embodiments, an exception handling step is also included, and this step specifically includes: If the local reflectivity is less than the preset reflectivity threshold, it is determined as an uncompensable area, marked as a shadow area and the brightness of this partition is reduced.

[0055] Among them, the brightness of the partition marked as the shadow area is adjusted to 0.3 times of the target brightness.

[0056] S800, driving the projection light source output according to the compensation brightness, and updating the reflectivity distribution data in real time.

[0057] The embodiments of this specific implementation method are all preferred embodiments of the present application, and are not intended to limit the protection scope of the present application. Therefore, all equivalent changes made based on the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for adjusting the brightness of a projector, characterized in that: The following steps are involved: Obtain the reflectivity distribution data of the projection area surface in real time and calculate the local reflectivity gradient; The projection area is non-uniformly partitioned according to the magnitude of the local reflectivity gradient; For each partition, the compensation brightness is calculated based on the reflectivity distribution data and the target brightness; The projection light source output is driven according to the compensation brightness, and the reflectivity distribution data is updated in real time.

2. The projector brightness adjustment method according to claim 1, characterized in that: The step of acquiring the reflectivity distribution data of the projection area surface in real time and calculating the local reflectivity gradient specifically includes: Based on the acquisition position of the projection area, the reflected light intensity, the test light intensity and the surface normal inclination angle are obtained, and the calculation formula of the reflectivity distribution data at the position (x, y) is: ； in, is the reflected light intensity, To test the light intensity, is the surface normal inclination, k is the sensor calibration coefficient; The calculation formula of the local reflectivity gradient at position (x, y) is: ； In the formula, and The discretized reflectivity matrix ρ[i][j] is obtained by calculating the partial derivative using the central difference method. The formula is: ； ； Where Δx, Δy are the sensor pixel pitches.

3. The projector brightness adjustment method according to claim 1, characterized in that: The step of non-uniformly partitioning the projection area according to the amplitude of the local reflectivity gradient specifically includes: Divide the projection area into initial large partitions according to preset specifications; Comparing the local reflectivity gradient of the initial large partition with a preset gradient threshold, if it is greater than the gradient threshold, splitting the initial large partition into multiple sub-partitions, and recalculating the local reflectivity gradient of each sub-partition; After each split, the local reflectivity gradient of each sub-partition is re-compared with the preset gradient threshold until all partitions pass the judgment; wherein, passing the judgment means that the local reflectivity gradient is greater than the gradient threshold, or the sub-partition size reaches the minimum preset size.

4. The projector brightness adjustment method according to claim 3, characterized in that: The step of non-uniformly partitioning the projection area according to the amplitude of the local reflectivity gradient also includes: Traverse all partitions and detect whether adjacent partitions meet the merging condition; the merging condition is that the local reflectivity gradient is less than or equal to half of the gradient threshold, and the difference in reflectivity mean is less than the preset difference; Merge the adjacent partitions that meet the conditions into superpixel partitions; A morphological erosion operation is performed on the merged partition boundaries to eliminate jagged edges.

5. The projector brightness adjustment method according to claim 1, characterized in that: The calculation formula of the compensation brightness is: ； In the formula, is the target brightness, is the local reflectivity, is the global average reflectivity, is the contrast adaptive weight; The local reflectivity refers to the average reflectivity of the partition.

6. The projector brightness adjustment method according to claim 5, characterized in that: The calculation formula of the local reflectivity is: ； In the formula, is the physical area of ​​the ith partition; Or / and, the calculation formula of the local reflectivity is: ； In the formula, The partition area.

7. The projector brightness adjustment method according to claim 5, characterized in that: The contrast adaptive weight adjustment method is: When the dynamic range of the image content is greater than the preset value, the calculation formula of the contrast adaptive weight is: ； In the formula, is the dynamic range; When fast switching of the screen is detected, the contrast adaptive weight decays exponentially, and its calculation formula is: ； Where τ is the decay time constant; The calculation formula of dynamic range is: ； In the formula, is the highest brightness value of all partitions in the entire projection area, It is the minimum brightness value of all partitions in the entire projection area.

8. The projector brightness adjustment method according to claim 7, characterized in that: The step of detecting fast switching of pictures is: if the inter-frame difference method detects fast switching, and at least one of the optical flow speed or the scene cutting detects fast switching.

9. The projector brightness adjustment method according to claim 5, characterized in that: It also includes exception handling steps: If the local reflectivity is less than the preset reflectivity threshold, it is determined as an uncompensable area, marked as a shadow area and the brightness of the area is reduced.

10. The projector brightness adjustment method according to claim 9, characterized in that: The brightness of the subarea marked as the shadow area is adjusted to 0.3 times the target brightness.

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