Automatic image tuning method based on monitoring equipment

Abstract

The invention relates to an automatic image tuning method based on monitoring equipment, which comprises the following steps of: 1, detecting and analyzing ambient brightness, and judging exposure time according to an analysis result; 2, judging whether a moving object exists in a video picture or not, and when the monitoring picture is completely static and the moving object is not detected, linkage coding parameter adjustment is carried out, the transmission data volume is reduced, and meanwhile, automatic linkage of image parameters is carried out; when a moving object is detected, coding parameter adjustment is linked, transmission data is increased, and image parameters are linked at the same time. According to the invention, the technical problem of poor image quality of the moving object in different environments can be solved.

Description

Technical Field

[0001] The invention belongs to the technical field of intelligent monitoring, and relates to an automatic image optimization method, in particular to an automatic image optimization method based on monitoring equipment. Background Art

[0002] At present, existing monitoring equipment is not only used in outdoor scenes such as roads and villages, but also in homes. The monitoring products used in homes also have different requirements for video image effects. Consumer monitoring products usually use wifi or 4G network connection transmission, and have certain requirements for data transmission volume and bandwidth. They usually require clear images, low picture noise, and the lower the encoding bit rate, the better. At the same time, consumer monitoring products are usually used to monitor moving people or animals in the home / indoors, and have higher requirements for the clarity of moving objects, but lower requirements for completely still pictures.

[0003] However, the existing automatic image optimization methods based on monitoring equipment still have the following defects:

[0004] 1. Usually a fixed upper limit of exposure time is used. When the environment is bright, a longer exposure time will cause blurring of moving objects.

[0005] 2. Consumer surveillance equipment usually has a low bit rate, especially moving objects will introduce greater bit rate pressure, resulting in unclear moving objects and possible mosaic phenomena.

[0006] Therefore, in order to solve the above technical problems, the present invention proposes an automatic image optimization method based on a monitoring device.

[0007] After searching, no public documents of the prior art identical or similar to the present invention were found. Summary of the invention

[0008] The purpose of the present invention is to overcome the shortcomings of the prior art and to propose an automatic image optimization method based on a monitoring device, which can solve the technical problem of poor image quality of moving objects in different environments.

[0009] The present invention solves the practical problem by adopting the following technical solutions:

[0010] An automatic image tuning method based on monitoring equipment, comprising:

[0011] Step 1: Detect and analyze the ambient brightness, and determine the exposure time based on the analysis results;

[0012] Step 2: Determine whether there are moving objects in the video frame. When the monitoring frame is completely static and no moving objects are detected, link to adjust the encoding parameters to reduce the amount of transmitted data, and at the same time automatically link to the image parameters; when moving objects are detected, link to adjust the encoding parameters to increase the transmitted data, and at the same time link to the image parameters.

[0013] Moreover, the specific method of the said Step 1 is:

[0014] Analyze the ambient brightness, calculate the brightness value bv according to the statistical information source of the sensor sensor, and thus determine which gear the exposure time belongs to:

[0015] When bv > 50000, set the upper limit of the exposure time to 10 ms for automatic exposure; when 0 < bv < 50000, set the upper limit of the exposure time to 20 ms for automatic exposure; when bv < 0, set the exposure time according to the frame rate fps to 1 / fps * 1000 ms for automatic exposure.

[0016] Moreover, the specific steps of the said Step 2 include:

[0017] (1) There are no moving objects in the video frame Object = 0, and there are moving objects Object = 1;

[0018] Record the default bitrate Bitrate, the default quantization parameter QP, the default sharpening degree Sharpen, and the default high-frequency detail degree Texture;

[0019] (2) When Object = 0, reduce the bitrate bitrate = 2 / 3 * Bitrate, and at the same time adjust qp = QP * (1 + bitrate / Bitrate), the sharpening degree sharpen = 2 / 3 * Sharpen, and the high-frequency detail texture = Texture * (1 - sharpen / Sharpen);

[0020] (3) When Object = 1, there are moving objects, and restore the default bitrate, quantization parameter, sharpening degree, and high-frequency detail of the current environment;

[0021] (4) Repeat Step 2 after the moving objects disappear; repeat Step 1 according to the bv value when the ambient brightness changes.

[0022] Advantages and beneficial effects of the present invention:

[0023] 1. The present invention provides an automatic image optimization method based on a monitoring device. Based on the characteristics of consumer monitoring, when the monitoring screen is completely static and no moving objects are detected, the encoding parameters are adjusted in联动, reducing the amount of transmitted data. At the same time, the image parameters are automatically linked to save network bandwidth while ensuring that the quality of the video image does not decrease.

[0024] 2. When the present invention detects a moving object, it adjusts the encoding parameters in联动, increasing the transmitted data, and at the same time links the image parameters to obtain a clear effect of the moving object.

[0025] 3. The present invention can automatically adjust the upper limit of the exposure time according to the indoor environmental brightness. When the environment is bright, the best effect of the moving object can be obtained; when the environment is relatively dim, the upper limit of the exposure time is released to ensure the overall image quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is the processing flowchart of the present invention;

[0027] Figure 2 is the flowchart for judging the exposure time gear of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0028] The following further details the embodiments of the present invention with reference to the accompanying drawings:

[0029] An automatic image optimization method based on a monitoring device, as Figure 1 and Figure 2 shown, includes:

[0030] Step 1: Detect and analyze the environmental brightness, and judge the exposure time according to the analysis result;

[0031] The specific method of the said Step 1 is:

[0032] Analyze the environmental brightness, calculate the brightness value Brightnessvalue - bv according to the statistical information source of the sensor sensor, and judge which gear the exposure time belongs to:

[0033] When bv > 50000, the upper limit of the exposure time is set to 10ms for automatic exposure; when 0 < bv < 50000, the upper limit of the exposure time is set to 20ms for automatic exposure; when bv < 0, the exposure time is set to 1 / fps * 1000ms according to the frame rate fps for automatic exposure.

[0034] Step 2: Determine whether there is a moving object in the video image. When the monitoring image is completely still and no moving object is detected, the coding parameters are adjusted to reduce the amount of transmitted data and automatically linked to the image parameters. When a moving object is detected, the coding parameters are adjusted to increase the amount of transmitted data and the image parameters are automatically linked.

[0035] The specific steps of step 2 include:

[0036] (1) There is no moving object in the video image Object = 0, there is a moving object Object = 1;

[0037] Record the default bitrate, default quantization parameter QP (Quantizer Parameter), default sharpening level Sharpen and default high-frequency detail level Texture;

[0038] (2) When Object = 0, reduce the bitrate = 2 / 3*Bitrate, and adjust qp = QP*(1+bitrate / Bitrate), sharpen = 2 / 3*Sharpen, and high-frequency detail texture = Texture*(1-sharpen / Sharpen) to reduce the pressure on network bandwidth and data volume while ensuring the basic clarity of the picture.

[0039] (3) When Object = 1, there is a moving object, and the default bit rate, quantization parameter, sharpness, and high-frequency details of the current environment are restored.

[0040] (4) When the moving object disappears, repeat step 2; when the ambient brightness changes, repeat step 1 according to the bv value.

[0041] The working principle of the present invention is:

[0042] In step 1 of the present invention, according to whether there is a moving object in the video screen, the video encoding parameters, image parameters, and exposure time are automatically adjusted to obtain the optimal moving object effect and save data transmission. The indoor ambient brightness is automatically detected, and different exposure time adjustable gears are set according to the ambient brightness.

[0043] In step 2 of the present invention, the length of the exposure time has a greater impact on the clarity of the moving object. The shorter the exposure time, the clearer the edge contour of the moving object.

[0044] When there is no moving object in the video image, the encoding bit rate is automatically reduced, the quantization parameter QP (Quantizer Parameter) is increased, the image parameters are linked, the sharpness and high-frequency detail parameters are reduced, and the image is guaranteed to have a certain degree of clarity, no obvious mosaics, and smaller storage.

[0045] In step 2 of the present invention, when a moving object is detected in the video picture, the encoding bit rate is automatically increased, the QP is reduced, the image parameters are linked, the sharpness and high-frequency details are restored, and the clarity of the moving object is ensured.

[0046] It should be emphasized that the embodiments of the present invention are illustrative rather than restrictive. Therefore, the present invention includes but is not limited to the embodiments described in the specific implementation modes. Any other implementation modes derived by those skilled in the art based on the technical solutions of the present invention also fall within the scope of protection of the present invention.

Claims

1. An automatic image tuning method based on monitoring equipment, characterized in that: Including: Step 1: Detect and analyze the ambient brightness of the system, and determine the exposure time according to the analysis result; Step 2: Determine whether there are moving objects in the video frame. When the monitoring frame is completely static and no moving objects are detected, link to adjust the encoding parameters to reduce the amount of transmitted data, and at the same time automatically link to the image parameters. When moving objects are detected, link to adjust the encoding parameters to increase the transmitted data, and at the same time link to the image parameters.

2. The automatic image tuning method based on monitoring equipment according to claim 1, characterized in that: The specific method of Step 1 is: Analyze the ambient brightness, calculate the brightness value bv according to the statistical information source of the sensor sensor, and determine which gear the exposure time belongs to: When bv>50000, the upper limit of the exposure time is set to 10ms for automatic exposure. When 0<bv<50000, the upper limit of the exposure time is set to 20ms for automatic exposure. When bv<0, the exposure time is set to 1 / fps*1000ms according to the frame rate fps for automatic exposure.

3. The automatic image tuning method based on monitoring equipment according to claim 1, characterized in that: The specific steps of Step 2 include: (1) There are no moving objects in the video frame Object = 0, and there are moving objects Object = 1; Record the default bitrate Bitrate, default quantization parameter QP, default sharpening degree Sharpen, and default high-frequency detail degree Texture; (2) When Object = 0, reduce the bitrate bitrate = 2 / 3*Bitrate, and at the same time adjust qp = QP*(1 + bitrate / Bitrate), sharpening degree sharpen = 2 / 3*Sharpen, high-frequency detail texture = Texture*(1 - sharpen / Sharpen); (3) When Object = 1, there are moving objects, and restore the default bitrate, quantization parameter, sharpening degree, and high-frequency details of the current environment; (4) Repeat Step 2 after the moving object disappears; repeat Step 1 according to the bv value when the ambient brightness changes.

Images