Method for improving image effect through intelligent strong light suppression

Abstract

The invention relates to a method for improving an image effect through intelligent strong light suppression, and the method comprises the following steps: 1, collecting a new frame of video, segmenting a picture, and calculating the brightness of each block; 2, calculating the average brightness of the current picture; 3, determining whether the object is a moving object or not by comparing the brightness difference of each block of the previous frame of image; and 4, when the object is judged to be a moving object, further judging whether the object is highlighted or not, and performing brightness suppression on the highlighted object by increasing a brightness weight. According to the invention, different strong light sources can be considered, different treatment is carried out, and different processing is carried out on key monitoring objects such as static unconcerned street lamps, dynamic vehicle lamps, flashlights and the like, so that an intelligent strong light inhibition effect is achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of video acquisition and monitoring, and relates to a method for suppressing strong light, and in particular to a method for improving image effects through intelligent strong light suppression. Background Art

[0002] Current video surveillance at night with low light levels will cause the image to be overexposed or too dark due to the influence of strong light such as street lights and car lights, and the image brightness needs to be adjusted manually. However, the common strong light suppression method does not distinguish between street lights and car lights, resulting in that after turning on the strong light suppression, the effect is either not strong, the image is still overexposed, and self-luminous objects such as cars cannot be seen clearly; or the intensity is too high, the image is relatively dark, and non-self-luminous objects such as pedestrians cannot be seen clearly.

[0003] Therefore, the present invention proposes a method for improving image quality through intelligent strong light suppression, which can solve the above problems.

[0004] After searching, no public documents of the prior art identical or similar to the present invention were found. Summary of the invention

[0005] The purpose of the present invention is to overcome the shortcomings of the prior art and propose a method for improving image effects through intelligent strong light suppression, which can take into account the sources of different strong lights and treat them differently. Different treatments are performed on static and unconcerned street lights and dynamic car lights, flashlights and other key monitored objects to achieve intelligent strong light suppression effects.

[0006] The present invention solves the practical problem by adopting the following technical solutions:

[0007] A method for improving image quality by intelligent strong light suppression comprises the following steps:

[0008] Step 1: Collect a new frame of video, divide the picture, and calculate the brightness of each piece;

[0009] Step 2, calculate the average brightness of the current picture;

[0010] Step 3: Determine whether it is a moving object by comparing the brightness difference of each block of the previous frame image;

[0011] Step 4: When it is determined to be a moving object, further determine whether it is a highlighted object, and suppress the brightness of the highlighted object by increasing the brightness weight.

[0012] Moreover, the specific method of step 1 is:

[0013] A new frame of video is captured, the newly captured picture is divided into 16x16 blocks, and the brightness Li (i=0-255) of each block is counted.

[0014] Moreover, the specific method of step 2 is as follows:

[0015] Calculate the average brightness Ave of the current screen: Ave = ∑Li / N; Li is the brightness value of each block, (i = 0 to 255); N is the number of blocks counted, which is 16x16 here.

[0016] Moreover, the specific steps of step 3 include:

[0017] (1) Compare the brightness difference of each block in the previous frame, Di = Li – Lprei;

[0018] where Di is the brightness difference after comparison, Li is the current frame brightness, Lprei is the previous frame brightness, and i represents the corresponding block, (i = 0 to 255);

[0019] (2) Determine whether it is a moving area according to the difference. If the absolute value of the difference is less than the threshold |Di| < Thresh, it is a static block, otherwise, if the difference is large, it is a moving block;

[0020] (3) Further distinguish dynamic objects and static objects, and do not perform strong light suppression on the selected static objects, and do not include them in the scope of strong light suppression.

[0021] Moreover, the specific steps of step 4 include:

[0022] (1) Statistically calculate the average brightness of all blocks with increased brightness in the moving area: Statistically calculate the average brightness AveH of all blocks with increased brightness in the moving area: AveH = ∑LHi / NH;

[0023] where LHi is the brightness of the block that has become brighter compared with the previous statistical information, and NH is the total number of blocks that have become brighter;

[0024] (2) Perform strong light suppression through weighted average brightness, and calculate the new average brightness Lum = Ave * (1 - k) + AveH * (k);

[0025] where k is the selected strong light suppression coefficient, and the value range is [0, 1];

[0026] (3) After comparing the weighted brightness Lum with the reference target brightness Target, perform subsequent exposure adjustment: If the weighted brightness is high, reduce the screen brightness, and if the weighted brightness is low, increase the screen brightness.

[0027] Advantages and beneficial effects of the present invention:

[0028] The present invention provides a method for improving image effects through intelligent strong light suppression. On the basis of monitoring continuous video, dynamic objects and static objects are distinguished by the difference of continuous images in the time domain. Strong light suppression is not performed on the selected static objects, and they are not included in the strong light suppression range. By further judging whether the objects determined to be moving are highlighted, the brightness of the highlighted objects is suppressed by increasing the brightness weight. The present invention takes into account the monitoring focus of dynamic luminous objects while avoiding the influence of fixed luminous bodies on the images of other surrounding objects. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a process flow chart of the present invention. DETAILED DESCRIPTION

[0030] The embodiments of the present invention are further described in detail below with reference to the accompanying drawings:

[0031] A method for improving image quality through intelligent strong light suppression, such as Figure 1 As shown, the following steps are included:

[0032] Step 1: Collect a new frame of video, divide the picture, and calculate the brightness of each piece;

[0033] The specific method of step 1 is:

[0034] First, in order to improve the calculation speed, a new frame of video is collected, the newly collected picture is divided into 16x16 blocks, and the brightness Li (i=0~255) of each block is counted;

[0035] Step 2, calculate the average brightness of the current picture;

[0036] The specific method of step 2 is:

[0037] Calculate the average brightness of the current picture Ave = ∑Li / N; Li is the brightness value of each block, (i = 0 to 255); N is the number of blocks counted, here 16x16.

[0038] Step 3: Determine whether it is a moving object by comparing the brightness difference of each block of the previous frame image;

[0039] The specific steps of step 3 include:

[0040] (1) Compare the brightness difference of each block in the previous frame image, Di = Li – Lprei;

[0041] Where Di is the brightness difference after comparison, Li is the brightness of the current frame, Lprei is the brightness of the previous frame, and i represents the corresponding block (i=0~255);

[0042] (2) Confirm whether it is a moving area based on the difference. If the absolute value of the difference is less than the threshold |Di| < Thresh, it is a stationary fast; otherwise, if the difference is large, it is a moving block.

[0043] (3) Further distinguish dynamic objects and static objects. For the selected static objects, no strong light suppression is performed and they are not included in the scope of strong light suppression.

[0044] Step 4: When it is determined to be a moving object, further determine whether it is a highlighted situation. For the highlighted objects, perform brightness suppression by increasing the brightness weight.

[0045] The specific steps of the above Step 4 include:

[0046] (1) Statistically calculate the average brightness of all blocks with increasing brightness in the moving area: Statistically calculate the average brightness AveH = ∑LHi / NH of all blocks with increasing brightness in the moving area;

[0047] Where, LHi is the brightness of the block that has become brighter compared with the previous statistical information, and NH is the total number of blocks that have become brighter.

[0048] (2) Perform strong light suppression through weighted average brightness, and calculate the new average brightness Lum = Ave * (1 - k) + AveH * (k);

[0049] Where, k is the selected strong light suppression coefficient, and the value range is [0, 1].

[0050] (3) After comparing the weighted brightness Lum with the reference target brightness Target, perform subsequent exposure adjustment: If the weighted brightness is high, reduce the screen brightness; if the weighted brightness is low, increase the screen brightness.

[0051] Thus, it can quickly suppress the overexposed moving objects and also shield the problem of the screen being too dark caused by fixed strong lights such as street lights.

[0052] It should be emphasized that the embodiments described in the present invention are illustrative rather than restrictive. Therefore, the present invention includes but is not limited to the embodiments described in the specific embodiments. Any other embodiments obtained by those skilled in the art based on the technical solutions of the present invention also belong to the scope of protection of the present invention.

Claims

1. A method for improving image quality through intelligent strong light suppression, characterized in that: It includes the following steps: Step 1: Collect a new frame of video, segment the picture, and calculate the brightness of each block; Step 2: Calculate the average brightness of the current picture; Step 3: Determine whether it is a moving object by comparing the brightness difference of each block in the previous frame; Step 4: When it is determined to be a moving object, further judge whether it is a highlighted situation, and suppress the brightness of the highlighted object by increasing the brightness weight.

2. The method for improving image quality by intelligent strong light suppression according to claim 1, characterized in that: The specific method of the said Step 1 is: Collect a new frame of video, segment the newly collected picture into 16x16 blocks, and count the brightness Li (i = 0~255) for each block.

3. The method for improving image quality by intelligent strong light suppression according to claim 1, characterized in that: The specific method of the said Step 2 is: Calculate the average brightness of the current picture Ave = ∑Li / N; Li is the brightness value of each block, (i = 0~255); N is the number of counted blocks, which is 16x16 here.

4. The method for improving image quality by intelligent strong light suppression according to claim 1, characterized in that: The specific steps of the said Step 3 include: (1) Compare the brightness difference of each block in the previous frame, Di = Li – Lprei; where Di is the brightness difference after comparison, Li is the brightness of the current frame, Lprei is the brightness of the previous frame, and i represents the corresponding block, (i = 0~255); (2) Confirm whether it is a moving area according to the difference value. If the absolute value of the difference is less than the threshold |Di| < Thresh, it is a static block, otherwise, if the difference is large, it is a moving block; (3) Further distinguish dynamic objects and static objects, and do not perform strong light suppression on the selected static objects and do not include them in the scope of strong light suppression.

5. The method for improving image quality by intelligent strong light suppression according to claim 1, characterized in that: The specific steps of the said Step 4 include: (1) Statistically calculate the average brightness of all blocks with increased brightness in the moving area: Statistically calculate the average brightness of all blocks with increased brightness in the moving area AveH = ∑LHi / NH; where LHi is the brightness of the block that has become brighter after comparison with the previous statistical information, and NH is the total number of blocks that have become brighter; (2) Perform strong light suppression through weighted average brightness, and calculate the new average brightness Lum = Ave * (1 - k) + AveH * (k); where k is the selected strong light suppression coefficient, and the value range is [0, 1]; (3) After comparing the weighted brightness Lum with the reference target brightness Target, perform subsequent exposure adjustment: If the weighted brightness is high, reduce the picture brightness; if the weighted brightness is low, increase the picture brightness.

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