CN103096035B - Monitor with video optimization function - Google Patents
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- CN103096035B CN103096035B CN201210595353.1A CN201210595353A CN103096035B CN 103096035 B CN103096035 B CN 103096035B CN 201210595353 A CN201210595353 A CN 201210595353A CN 103096035 B CN103096035 B CN 103096035B
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Abstract
The invention discloses a monitor with a video optimization function. The monitor with the video optimization function comprises a display unit. The monitor with the video optimization function is characterized by further comprising a video optimization module. By using a field programmable gate array (FPGA) programmable logic and matching with a particular algorithm, the video optimization module conducts optimization to a video image, enables the video image to be clear and enables an object outline in the video image to be clear and discernible, and simultaneously sends the video image after the optimization to the display module to conduct driving display. The monitor with the video optimization function has the advantages that the quality of monitoring videos is further improved, the application of other auxiliary devices is avoided at the same time, and the monitor with the video optimization function is simple in structure, stable and reliable, low in cost and high in practicability.
Description
Technical field
The present invention relates to monitor field, in particular, it is a kind of monitor with video optimized function
Background technology
Visual information is the main source that the mankind obtain external information, because about 70% information is to be obtained by human eye
?.Develop rapidly with multimedia technology, video image has obtained extensive attention and applied, its application is throughout broadcast electricity
Depending on, medical science, guard monitor, parking lot management, the aspect such as military and life sciences.Video capture technology and the lifting of Display Technique,
Make the requirement more and more higher to image quality for the people, but the transmission of image and conversion are (as being imaged, again in all kinds of picture systems
System, scanning, transmission and display etc.) always cause the reduction of picture quality to a certain extent.Such as some outdoor monitoring systems
System often can only under fine day could normal work, the vile weathers such as dense fog, sand and dust or low light situation hypograph contrast
Degree substantially reduces, and people cannot therefrom obtain useful information.Moreover, the video of long-term viewing low quality may increase
The burden of people's eyes, easily produces visual fatigue, or even can have a dizzy spell.The vile weathers such as dense fog, heavy rain, sand and dust are occurring
When, the contrast of outdoor scene image and color all can be changed or degenerate, the many features containing in image be all capped or
Fuzzy, obtain is degraded image, all causes great difficulty for all kinds of monitoring, therefore, will give full play to supervision video
Efficiency, be necessary for monitor video image carry out enhancement process.In terms of military surveillance, supervision, in order to implement correctly to command,
Obtain triumph of fighting, modern war is put forward higher requirement to military surveillance, extensively apply advanced science and technology, expand further
The big scope scouted, improves the ageing and accuracy scouted.Therefore, military surveillance, monitor in the product of video image used
Matter is particularly important, and the video image of degeneration is comprehended with place to the identification of information and caused a deviation, and the consequence of this deviation is very
Serious, therefore video enhancement techniques are arisen at the historic moment.If video image enhancement is a tradition of field of video image processing
Topic, again always more active research field simultaneously, constantly there are new method and new tool to introduce and make this field keep vigorous
Vitality, and constantly have new results to emerge.Video enhancement techniques have been widely used for military affairs, medical science, remote sensing and video inspection
The aspects such as survey.
In military aspect, because viewing distance is remote and awful weather, the video of acquisition often has target and background to be obscured
Unclear, be difficult to distinguish etc., need to carry out enhancement process target interested could be highlighted；Medical science aspect, due to imaging
Inherent characteristicses, medical image is often very dark, smudgy, is difficult to identify pathological tissues and normal structure, needs image to increase
Strength reason is to project pathological tissues；Remote sensing aspect, such as agricultural monitoring using remote sensing, carrying out Crop classification needs application image to strengthen skill
Art is to improve the precision of classification；In terms of public safety, the process of portrait, fingerprint and other vestiges and identification, and follow the tracks of, thing
Therefore the application such as analysis, also it is required for image enhancement processing to project the feature of image.All kinds of video monitoring systems are also badly in need of
Video image enhancement function, to make up the deficiency of existing system, improves the contrast of video image, improves monitoring effect and target
Identification, improve the phenomenons such as uneven illumination is even, obtain visual effect more preferably, be conducive to analyzing further and interpretation video image.
With scientific and technological development, the demand that every profession and trade is processed to low visibility image, video source modeling at this stage will get more and more.
Content of the invention
The present invention is directed to the deficiencies in the prior art, provides a kind of monitor with video optimized function.
Concrete technical scheme of the present invention is as follows：
A kind of monitor with video optimized function, including display unit it is characterised in that also including
Video optimization modules, this module is passed through, using FPGA FPGA, to coordinate exclusive algorithm, by input monitor
Due to shooting environmental insufficient light or weather environment impact, the such as greasy weather, or due to transmission loss not clearly,
The image being difficult to recognize is optimized, and makes image clearly, and objects in images is clear-cut distinguishable, simultaneously by the image after optimizing
Deliver to display module to be driven showing, so that monitor has video optimized function.This module takes the Y of video image to divide
Spirogram picture is processed, and the gray value of Y-component is taken after log, calculate two pixels that any distance on image is (a, b) it
Between relationship between light and dark, further according to the gray value of each pixel in this relationship between light and dark correction map picture, and revised image Y is divided
Amount data sets up rectangular histogram, according to " the 3 σ principle " of Gauss distribution, the gray value in rectangular histogram is optimized, to be optimized
Gradation of image Value Data afterwards, then the merging of same U, V component data, realize the optimization function of image.
Further, described display unit is LCDs.
The present invention, compared with existing monitor, has advantages below：
1. provide a kind of monitor with video optimized function, supervise except meeting monitor in addition to the function of video,
Can also be according to setting, video content interested in user is optimized, and lifts the quality of monitor video further, avoids simultaneously
The application of other auxiliary equipments.
2. realize the optimization processing of video using FPGA FPGA, coordinate exclusive algorithm, system structure is simple, surely
Fixed reliable.
3. cost of the present invention is relatively low, and practicality is high.
Brief description
Fig. 1 has the composition block diagram of the monitor of video optimized function for the present invention.
Fig. 2 is the workflow diagram of video optimization modules of the present invention.
Fig. 3 is video image Nogata illustrated example.
Specific embodiment
The present invention is further illustrated below in conjunction with the accompanying drawings.
Fig. 1 has the composition block diagram of the monitor of video optimized function for the present invention.As illustrated, the video letter of input
Number it is converted into yuv format through Video decoding module decoding, enter in video optimization modules and be optimized process, input video is excellent
Turn to and there is the video signal of higher contrast and definition be sent to display module, and by video in LCDs
Display.Wherein DDR is the buffer memory device of video optimization modules, and video optimization modules adopt FPGA Programmadle logic chip to realize.
Fig. 2 is the workflow diagram of video optimization modules of the present invention.Video optimization modules adopt FPGA FPGA real
Existing, programmed by FPGA, realize the optimization function to inputted video image.As illustrated, decoded turn of the video signal of input
Change yuv format into, take wherein Y-component gradation data to be optimized in the steps below：
Step 1：If inputted video image size is m × n, inputted video image Y-component gradation data is Y (i, j), wherein
0≤i≤m, 0≤j≤n.Y-component gradation data Y ' (i, the j)=T of image after simultaneously supposing to optimize.
Step 2：The gradation data of Y (i, j) component is taken the logarithm process, that is,
Ylog(i, j)=logY (i, j) (formula 1)
Step 3：Calculating any two distance in image is the relative relationship between light and dark between the pixel of (a, b).
Take a=m/2, b=0 first, the relationship between light and dark of point-to-point transmission is
R (i, j)=Ylog(i+a, j+b)-Ylog(i, j) (formula 2)
According to this relationship between light and dark, the gray value of each of original image pixel is modified, draws new figure
As gray value data Y ' (i, j)：
Y ' (i, j)=T-R (i, j) (formula 3)
Y ' (i+a, j+b)=T+R (i, j) (formula 4)
Double counting is revised until the gradation data of each pixel；
Step 4：Take a=0, b=n/2, calculating any two distance in video image according to formula 2 is the pixel of (a, b)
Relationship between light and dark between point；
Formula 3, the gray value of each pixel in formula 4 correction map picture are passed through according to this relationship between light and dark, then schemes after revising
In picture, the gray value of pixel is Y ' (i, j)；
Double counting is revised until the gray value of each pixel；
Step 5：Take a to be the last time calculate a value 1/2, if a value non-integer, round fractional part, b=0 repeat step 3
The gray value data of correction map picture further；
Step 6：Take a=0, b to be the last time calculate b value 1/2, if b value non-integer, round fractional part, repeat step 4
The gray value data of correction map picture further；
Step 7：Repeat step 5 and step 6, the image gradation data Y ' until the value of a, b is respectively 1, after being corrected
(i, j)；
Step 8：Set up the rectangular histogram (as shown in Figure 3) of video image according to the gradation data of revised Y ' (i, j)；
Step 9：Process is optimized to the rectangular histogram of Y ' (i, j) according to " the 3 σ principle " of Gauss distribution；
In Gauss distribution (also referred to as normal distribution), μ is the average of the stochastic variable of Normal Distribution, and σ is random change
The standard deviation of amount, the probabilistic law of the stochastic variable of Normal Distribution be take the value neighbouring with μ probability big, and take and get over from μ
The probability of remote value is less.For normal random variable, the probability that its value falls in interval [μ -3 σ, μ+3 σ] is
99.74%, and the value falling outside interval only accounts for less than 0.3%, is almost negligible, here it is " 3 σ principle ".
According to " 3 σ rule ", the impact with the distance of the video image gradation data mean μ pixel more than 3 σ can
Ignore, thus we are only optimized to the gradation data between μ -3 σ to μ in rectangular histogram+3 σ, according to
Wherein D (i, j) is the video image gradation data after optimizing, and d is corresponding grey scale grade coordinate values in rectangular histogram, f (i,
J) function optimizing for rectangular histogram.
The gradation data of Y-component after optimizing and the data of U, V component are carried out image synthesis, form new contrast,
The higher video signal of definition, is sent to the display module of monitor, thus by the clear video image optimizing in liquid
Show on brilliant screen.
Claims (2)
1. a kind of monitor with video optimized function, including display unit it is characterised in that also including video optimized mould
Block, the video signal of input is decoded to be converted into yuv format, and video optimization modules take wherein Y-component gradation data by following steps
Suddenly it is optimized：
Step 1：If inputted video image size is m × n, inputted video image Y-component gradation data is Y (i, j), wherein 0≤i
≤ m, 0≤j≤n；Y-component gradation data Y ' (i, the j)=T of image after simultaneously supposing to optimize；
Step 2：The gradation data of Y (i, j) component is taken the logarithm process, that is,
Ylog(i, j)=logY (i, j) (formula 1)
Step 3：Calculating any two distance in image is the relative relationship between light and dark between the pixel of (a, b)；
Take a=m/2, b=0 first, the relationship between light and dark of point-to-point transmission is
R (i, j)=Ylog(i+a, j+b)-Ylog(i, j) (formula 2)
According to this relationship between light and dark, the gray value of each of original image pixel is modified, draws new image ash
Angle value data Y ' (i, j)：
Y ' (i, j)=T-R (i, j) (formula 3)
Y ' (i+a, j+b)=T+R (i, j) (formula 4)
Double counting is revised until the gradation data of each pixel；
Step 4：Take a=0, b=n/2, calculating any two distance in video image according to formula 2 is between the pixel of (a, b)
Relationship between light and dark；
Formula 3 is passed through according to this relationship between light and dark, the gray value of each pixel in formula 4 correction map picture, then in image after revising
The gray value of pixel is Y ' (i, j)；
Double counting is revised until the gray value of each pixel；
Step 5：Take a to be the last time calculate a value 1/2, if a value non-integer, round fractional part, b=0 repeat step 3 enters one
The gray value data of step correction map picture；
Step 6：Take a=0, b to be the last time calculate b value 1/2, if b value non-integer, round fractional part, repeat step 4 enters one
The gray value data of step correction map picture；
Step 7：Repeat step 5 and step 6, until the value of a, b is respectively 1, image gradation data Y ' after being corrected (i,
j)；
Step 8：Set up the rectangular histogram of video image according to the gradation data of revised Y ' (i, j)；
Step 9：Process is optimized to the rectangular histogram of Y ' (i, j) according to " the 3 σ principle " of Gauss distribution；
Only the gradation data between μ -3 σ to μ in rectangular histogram+3 σ is optimized according to formula 8；
Wherein D (i, j) is the video image gradation data after optimizing, and d is corresponding grey scale grade coordinate values in rectangular histogram, and f (i, j) is
The function that rectangular histogram optimizes；
The gradation data of Y-component after optimizing and the data of U, V component are carried out image synthesis, forms new contrast, clearly
Spend higher video signal, be sent to the display unit of monitor, thus will be single in display for the clear video image optimizing
Show in unit.
2. according to claim 1 monitor it is characterised in that described display unit be LCDs.
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| CN103702180B (en) * | 2014-01-14 | 2017-06-20 | 北京奇艺世纪科技有限公司 | media file playing method and device |
| CN105678717B (en) * | 2016-03-02 | 2018-10-23 | 优酷网络技术（北京）有限公司 | Dynamic video image clarity intensifying method and device |
| CN109218802B (en) * | 2018-08-23 | 2020-09-22 | Oppo广东移动通信有限公司 | Video processing method and device, electronic equipment and computer readable medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1347246A (en) * | 2000-09-30 | 2002-05-01 | Lg电子株式会社 | Video signal contrast intensifying device |
| CN101170640A (en) * | 2006-10-23 | 2008-04-30 | 宝山钢铁股份有限公司 | Furnace steel level monitoring system and image optimization device |
| CN101478670A (en) * | 2008-12-30 | 2009-07-08 | 西安交通大学 | Network real-time video collecting apparatus developed based on FPGA chip and DSP chip |
| CN202261654U (en) * | 2011-08-31 | 2012-05-30 | 窦浩 | FPGA (Field Programmable Gate Array) video image storing and processing device |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1347246A (en) * | 2000-09-30 | 2002-05-01 | Lg电子株式会社 | Video signal contrast intensifying device |
| CN101170640A (en) * | 2006-10-23 | 2008-04-30 | 宝山钢铁股份有限公司 | Furnace steel level monitoring system and image optimization device |
| CN101478670A (en) * | 2008-12-30 | 2009-07-08 | 西安交通大学 | Network real-time video collecting apparatus developed based on FPGA chip and DSP chip |
| CN202261654U (en) * | 2011-08-31 | 2012-05-30 | 窦浩 | FPGA (Field Programmable Gate Array) video image storing and processing device |
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