AU2020101501A4 - Image optimization system based on fitting approximation algorithm - Google Patents

Image optimization system based on fitting approximation algorithm Download PDF

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AU2020101501A4
AU2020101501A4 AU2020101501A AU2020101501A AU2020101501A4 AU 2020101501 A4 AU2020101501 A4 AU 2020101501A4 AU 2020101501 A AU2020101501 A AU 2020101501A AU 2020101501 A AU2020101501 A AU 2020101501A AU 2020101501 A4 AU2020101501 A4 AU 2020101501A4
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image
images
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module
unit
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AU2020101501A
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Zhenqi Yang
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Shandong Jiaotong University
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Shandong Jiaotong University
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/80—Geometric correction
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00—Geometric image transformations in the plane of the image
    • G06T3/20—Linear translation of whole images or parts thereof, e.g. panning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00—Geometric image transformations in the plane of the image
    • G06T3/40—Scaling of whole images or parts thereof, e.g. expanding or contracting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00—Geometric image transformations in the plane of the image
    • G06T3/60—Rotation of whole images or parts thereof
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/136—Segmentation; Edge detection involving thresholding
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/90—Determination of colour characteristics
    • G—PHYSICS
    • G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09G—ARRANGEMENTS OR CIRCUITS FOR CONTROL OF INDICATING DEVICES USING STATIC MEANS TO PRESENT VARIABLE INFORMATION
    • G09G5/00—Control arrangements or circuits for visual indicators common to cathode-ray tube indicators and other visual indicators
    • G09G5/12—Synchronisation between the display unit and other units, e.g. other display units, video-disc players
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10024—Color image
    • G—PHYSICS
    • G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09G—ARRANGEMENTS OR CIRCUITS FOR CONTROL OF INDICATING DEVICES USING STATIC MEANS TO PRESENT VARIABLE INFORMATION
    • G09G2300/00—Aspects of the constitution of display devices
    • G09G2300/02—Composition of display devices
    • G09G2300/023—Display panel composed of stacked panels
    • G—PHYSICS
    • G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09G—ARRANGEMENTS OR CIRCUITS FOR CONTROL OF INDICATING DEVICES USING STATIC MEANS TO PRESENT VARIABLE INFORMATION
    • G09G2320/00—Control of display operating conditions
    • G09G2320/06—Adjustment of display parameters
    • G—PHYSICS
    • G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09G—ARRANGEMENTS OR CIRCUITS FOR CONTROL OF INDICATING DEVICES USING STATIC MEANS TO PRESENT VARIABLE INFORMATION
    • G09G2360/00—Aspects of the architecture of display systems
    • G09G2360/04—Display device controller operating with a plurality of display units
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
    • H04N21/23—Processing of content or additional data; Elementary server operations; Server middleware
    • H04N21/242—Synchronization processes, e.g. processing of PCR [Program Clock References]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N5/00—Details of television systems
    • H04N5/04—Synchronising
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N5/00—Details of television systems
    • H04N5/04—Synchronising
    • H04N5/06—Generation of synchronising signals
    • H04N5/067—Arrangements or circuits at the transmitter end
    • H04N5/073—Arrangements or circuits at the transmitter end for mutually locking plural sources of synchronising signals, e.g. studios or relay stations
    • H04N5/0733—Arrangements or circuits at the transmitter end for mutually locking plural sources of synchronising signals, e.g. studios or relay stations for distributing synchronisation pulses to different TV cameras

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Multimedia (AREA)
  • Computer Hardware Design (AREA)
  • Image Processing (AREA)

Abstract

The present invention discloses an image optimization system based on a fitting approximation algorithm. The system includes a network synchronization module, a shape and position correction module, a color correction module, a fitting approximation module and an image synthesis module. Through the fitting approximation algorithm, the system well realizes the synchronous playing effect of a plurality of sub-computers. Image optimization solutions provided by the system are applicable to various shapes of display screens, so that not only the network delay dynamics between a host computer and each sub-computer can be effectively controlled and a better synchronous playing effect be realized, but also a better image display effect can be achieved through the color correction and shape and position correction. The system is stable, reliable, and more suitable for being popularized and applied in a large range. Drawings of Description Fi. Network synchronization module 2 Shape and position correction module Image synthesis 3 __module Color correction module 4 Fitting approximation module Fig. 2 21 Shape and position error acquisition unit 22 Error integration unit 23 Error compensation unit Fig. 2 1

Description

Drawings of Description
Network synchronization module 2
Shape and position correction module
Image synthesis 3 Fi.__module
Color correction module
4
Fitting approximation module
Fig. 2
21
Shape and position error acquisition unit
22
Error integration unit
23
Error compensation unit
Fig. 2
Description
IMAGE OPTIMIZATION SYSTEM BASED ON FITTING APPROXIMATION ALGORITHM
Technical Field
The present invention relates to the technical field of image processing, and more particularly relates to an image optimization system based on a fitting approximation algorithm.
Background At present, as requirements of people for the comfort of electronic equipment screens are gradually increased, the screen size is increased while the definition requirements are also more stringent. The image quality requirements of screens such as curved screens and arc screens are relatively higher. How to meet the needs of large-screen display and the display of various shapes of screens and also to meet the watching need of users for the high-quality images has become the focus of many scholars. The existing large-sized screen is generally formed by splicing a plurality of display units. During the image display, the plurality of display units play synchronously. However, due to the network delay between sub-computers corresponding to the plurality of display units and a host computer during the image playing process and the color difference of the image caused by the illumination of light in different areas, the image quality of the large-sized screen and various curved and arc screens is greatly affected by the above factors, so that the requirements of people for the image display of the high-quality, large-sized and curved screens are difficult to meet.
Therefore, how to provide a stable and reliable image optimization system is a problem to be urgently solved by those skilled in the art.
Description
Summary In view of this, the present invention provides an image optimization system based on a fitting approximation algorithm. By reasonably using the fitting approximation algorithm, the system compensates the network delay during the synchronous playing of a plurality of sub-computers and performs the color and shape and position correction, thereby solving the problem that the existing large-screen image processing mode is poor in image display effect. To realize the above purpose, the present invention adopts the following technical solutions: An image optimization system based on a fitting approximation algorithm includes: a network synchronization module, wherein the network synchronization module is used to calculate network delay between a host computer and each sub-computer and compensate the calculated network delay; a shape and position correction module, wherein the shape and position correction module is used to perform the integrated computation for a shape and position error between the host computer and each sub-computer to obtain an input-output coordinate value; a color correction module, wherein the color correction module is used to perform the deviation correction for display brightness and color of the host computer and each sub-computer; a fitting approximation module, wherein the fitting approximation module performs the transform processing for an image of each sub-computer according to preset parameters of the host computer and each sub-computer to approximate a target correction curved surface and to perform the fitting for a color curve generated by the light difference in a fusion area of the images; and
Description
an image synthesis module, wherein the image synthesis module synthesizes a target image according to an image processing result of the host computer and each sub-computer and outputs the target image. Further, the shape and position correction module includes a shape and position error acquisition unit, an error integration unit and an error compensation unit. The shape and position error acquisition unit is used to calculate a shape and position error between the host computer and each sub-computer respectively. The error integration unit is used to perform the integrated computation for the shape and position error of the host computer and each sub-computer. The error compensation unit is used to compensate the shape and position error obtained by the integrated computation. In the system provided by the present invention, the shape and position errors between the host computer and each sub-computer are integrated and further calculated so as to accurately calculate the coordinate value input and output, thereby correctly compensating the shape and position error. Further, the color correction module includes an image acquisition unit, an image classification unit, a highlight extraction unit, a clustering unit, a correction unit and a weighted integration unit. The image acquisition unit is used to acquire a display image of the host computer and each sub-computer. The image classification unit is used to perform the soft classification for the display image to obtain three types of reddish, greenish and blueish images. The highlight extraction unit is used to calculate a proportionality coefficient of each type of images respectively, and to perform the highlight extraction for each type of images respectively according to the proportionality coefficient of each type of images and an image affection factor to obtain a plurality of highlight areas of each type of images. The image affection factor is any value between 6% and 14%.
Description
The clustering unit is used to perform chromaticity clustering and spatial clustering for the plurality of highlight areas of each type of images to obtain a plurality of reference light sources of each type of images. The correction unit is used to calculate a correction coefficient of the type of images according to a chromaticity distance and a spatial distance between all reference light sources in each type of images and all pixels of the type of images and to correct each pixel in the type of images according to the obtained correction coefficient to obtain a corrected image of the types of images. The weighted integration unit is used to perform the weighted integration for the obtained three types of corrected images to obtain a corrected image of the display image. Through the above process, a color curve generated by the difference of the light in the fusion area can be fit, and on the premise of not affecting the image quality, a size of the fusion area is greatly reduced. More further, a highlight extraction process of the highlight extraction unit specifically includes the following steps: Step 1: calculating a highlight extraction threshold value of three types of images respectively; Step 2: extracting a plurality of highlight pixels in the three types of images respectively according to the highlight extraction threshold values and channel values; Step 3: according to all highlight pixels in each type of images, creating a new image corresponding to the type of images; Step 4: performing binarization processing for each type of new images to obtain a binarized image corresponding to the type of images; and Step 5: clearing isolated pixels and pixels communicated only in one direction in the binarized images to obtain a plurality of highlight areas of each type of images.
Description
The three types of reddish, greenish, and blueish images mentioned above correspond to R, G and B three channels respectively. It can be known from the above technical solutions that compared with the prior art, the present invention discloses the image optimization system based on the fitting approximation algorithm. Through the fitting approximation algorithm, the system well realizes the synchronous playing effect of the plurality of sub-computers. The image optimization solutions provided by the system are applicable to various shapes of display screens, so that not only the network delay between the host computer and each sub-computer can be effectively controlled and a better synchronous playing effect be realized, but also a better image display effect can be achieved through the color correction and the shape and position correction. The system is stable, reliable, and more suitable for being popularized and applied in a large range.
Description of Drawings To more clearly describe the technical solution in the embodiments of the present invention or in the prior art, the drawings required to be used in the description of the embodiments or the prior art will be simply presented below. Apparently, the drawings in the following description are merely the embodiments of the present invention, and for those ordinary skilled in the art, other drawings can also be obtained according to the provided drawings without contributing creative labor. Fig. 1 is a structural schematic diagram of an overall architecture of an image optimization system based on a fitting approximation algorithm provided by the present invention; Fig. 2 is a structural schematic diagram of an architecture of a shape and position correction module in embodiments of the present invention;
Description
Fig. 3 is a structural schematic diagram of an architecture of a color correction module in embodiments of the present invention; and
Fig. 4 is a flow chart of a highlight extraction process of a highlight extraction unit in embodiments of the present invention.
Detailed Description The technical solution in the embodiments of the present invention will be clearly and fully described below in combination with the drawings in the embodiments of the present invention. Apparently, the described embodiments are merely part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by those ordinary skilled in the art without contributing creative labor will belong to the protection scope of the present invention. By referring to Fig. 1, embodiments of the present invention disclose an image optimization system based on a fitting approximation algorithm. The system includes: a network synchronization module 1, wherein the network synchronization module 1 is used to calculate network delay between a host computer and each sub-computer and compensate the calculated network delay; a shape and position correction module 2, wherein the shape and position correction module 2 is used to perform the integrated computation for a shape and position error between the host computer and each sub-computer to obtain an input-output coordinate value; a color correction module 3, wherein the color correction module 3 is used to perform the deviation correction for display brightness and color of the host computer and each sub-computer; a fitting approximation module 4, wherein the fitting approximation module 4 performs the transform processing for an image of each sub-computer according to
Description
preset parameters of the host computer and each sub-computer to approximate a target correction curved surface and to perform the fitting for a color curve generated by the light difference in a fusion area of the images; and an image synthesis module 5, wherein the image synthesis module 5 synthesizes a target image according to an image processing result of the host computer and each sub-computer and outputs the target image. In a specific embodiment, by referring to Fig. 2, the shape and position correction module 2 includes a shape and position error acquisition unit 21, an error integration unit 22 and an error compensation unit 23. The shape and position error acquisition unit 21 is used to calculate a shape and position error between the host computer and each sub-computer respectively. The error integration unit 22 is used to perform the integrated computation for the shape and position error of the host computer and each sub-computer. The error compensation unit 23 is used to compensate the shape and position error obtained by the integrated computation. In a specific embodiment, by referring to Fig. 3, the color correction module 3 includes an image acquisition unit 31, an image classification unit 32, a highlight extraction unit 33, a clustering unit 34, a correction unit 35 and a weighted integration unit 36. The image acquisition unit 31 is used to acquire a display image of the host computer and each sub-computer. The image classification unit 32 is used to perform the soft classification for the display image to obtain three types of reddish, greenish and blueish images. The highlight extraction unit 33 is used to calculate a proportionality coefficient of each type of images respectively, and to perform the highlight extraction for each type of images respectively according to the proportionality coefficient of each type of images and an image affection factor to obtain a plurality of highlight areas of each type of images.
Description
The clustering unit 34 is used to perform chromaticity clustering and spatial clustering for the plurality of highlight areas of each type of images to obtain a plurality of reference light sources of each type of images. The correction unit 35 is used to calculate a correction coefficient of the type of images according to a chromaticity distance and a spatial distance between all reference light sources in each type of images and all pixels of the type of images and to correct each pixel in the type of images according to the obtained correction coefficient to obtain a corrected image of the types of images. The weighted integration unit 36 is used to perform the weighted integration for the obtained three types of corrected images to obtain a corrected image of the display image. Specifically, the image affection factor mentioned in the above embodiment is any value between 6% and 14%. Specifically, in the present embodiment, the three types of reddish, greenish, and blueish images correspond to R, G and B three channels respectively. In a specific embodiment, by referring to Fig. 4, a highlight extraction process of the highlight extraction unit 33 in the present embodiment specifically includes the following steps: Step 1: calculating a highlight extraction threshold value of three types of images respectively; Step 2: extracting a plurality of highlight pixels in the three types of images respectively according to the obtained highlight extraction threshold values and channel values; Step 3: according to all highlight pixels in each type of images, creating a new image corresponding to the type of images respectively; Step 4: performing binarization processing for each type of new images to obtain a binarized image corresponding to the type of images; and
Description
Step 5: clearing isolated pixels and pixels communicated only in one direction in the binarized images to obtain a plurality of highlight areas of each type of images. Specifically, in the present embodiment, the fitting approximation module 4 performs transform processing for the images, wherein the processing content includes zooming, translation, rotation, twisting, etc. By zooming in, zooming out, translating, rotating and twisting the images, it is ensured that after the image is projected onto a screen, the content distribution is more uniform and regular. In conclusion, compared with the prior art, the image optimization system based on the fitting approximation algorithm disclosed by the present invention has the following advantages: The system architecture is established by the fitting approximation algorithm, so that the synchronous playing effect of the plurality of sub-computers can be well realized. The system is applicable to various shapes of display screens, thereby not only effectively controlling the network delay dynamics between the host computer and each sub-computer and realizing a better synchronous playing effect, but also achieving a better image display effect through the color correction and the shape and position correction. The system structure is simple, stable and reliable. Each embodiment in the description is described in a progressive way. The difference of each embodiment from each other is the focus of explanation. The same and similar parts among all of the embodiments can be referred to each other. For a device disclosed by the embodiments, because the device corresponds to a method disclosed by the embodiments, the device is simply described. Refer to the description of the method part for the related part. The above description of the disclosed embodiments enables those skilled in the art to realize or use the present invention. Many modifications to these embodiments will be apparent to those skilled in the art. The general principle defined herein can be realized in other embodiments without departing from the
Description
spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principle and novel features disclosed herein.

Claims (4)

Claims
1. An image optimization system based on a fitting approximation algorithm, comprising: a network synchronization module, wherein the network synchronization module is used to calculate network delay between a host computer and each sub-computer and compensate the calculated network delay; a shape and position correction module, wherein the shape and position correction module is used to perform the integrated computation for a shape and position error between the host computer and each sub-computer to obtain an input-output coordinate value; a color correction module, wherein the color correction module is used to perform the deviation correction for display brightness and color of the host computer and each sub-computer; a fitting approximation module, wherein the fitting approximation module performs the transform processing for an image of each sub-computer according to preset parameters of the host computer and each sub-computer to approximate a target correction curved surface and to perform the fitting for a color curve generated by the light difference in a fusion area of the images; and an image synthesis module, wherein the image synthesis module synthesizes a target image according to an image processing result of the host computer and each sub-computer and outputs the target image.
2. The image optimization system based on the fitting approximation algorithm according to claim 1, wherein the shape and position correction module comprises a shape and position error acquisition unit, an error integration unit and an error compensation unit; the shape and position error acquisition unit is used to calculate a shape and position error between the host computer and each sub-computer respectively; the error integration unit is used to perform the integrated computation for the shape and position error of the host computer and each sub-computer;
Claims
the error compensation unit is used to compensate the shape and position error obtained by the integrated computation.
3. The image optimization system based on the fitting approximation algorithm according to claim 1, wherein the color correction module comprises an image acquisition unit, an image classification unit, a highlight extraction unit, a clustering unit, a correction unit and a weighted integration unit; the image acquisition unit is used to acquire a display image of the host computer and each sub-computer; the image classification unit is used to perform the soft classification for the display image to obtain three types of reddish, greenish and blueish images; the highlight extraction unit is used to calculate a proportionality coefficient of each type of images respectively, and to perform the highlight extraction for each type of images respectively according to the proportionality coefficient of each type of images and an image affection factor to obtain a plurality of highlight areas of each type of images; and the image affection factor is any value between 6% and 14%; the clustering unit is used to perform chromaticity clustering and spatial clustering for the plurality of highlight areas of each type of images to obtain a plurality of reference light sources of each type of images; the correction unit is used to calculate a correction coefficient of the type of images according to a chromaticity distance and a spatial distance between all reference light sources in each type of images and all pixels of the type of images and to correct each pixel in the type of images according to the obtained correction coefficient to obtain a corrected image of the types of images; the weighted integration unit is used to perform the weighted integration for the obtained three types of corrected images to obtain a corrected image of the display image.
Claims
4. The image optimization system based on the fitting approximation algorithm according to claim 3, wherein a highlight extraction process of the highlight extraction unit specifically comprises the following steps: calculating a highlight extraction threshold value of three types of images respectively; extracting a plurality of highlight pixels in the three types of images respectively according to the highlight extraction threshold values and channel values; according to all highlight pixels in each type of images, creating a new image corresponding to the type of images; performing binarization processing for each type of new images to obtain a binarized image corresponding to the type of images; and clearing isolated pixels and pixels communicated only in one direction in the binarized images to obtain a plurality of highlight areas of each type of images.
AU2020101501A 2020-07-27 2020-07-27 Image optimization system based on fitting approximation algorithm Ceased AU2020101501A4 (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112884682A (en) * 2021-01-08 2021-06-01 福州大学 Stereo image color correction method and system based on matching and fusion
CN114140336A (en) * 2021-10-08 2022-03-04 中国安全生产科学研究院 Infrared image-based dead pixel processing method and device

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112884682A (en) * 2021-01-08 2021-06-01 福州大学 Stereo image color correction method and system based on matching and fusion
CN112884682B (en) * 2021-01-08 2023-02-21 福州大学 Stereo image color correction method and system based on matching and fusion
CN114140336A (en) * 2021-10-08 2022-03-04 中国安全生产科学研究院 Infrared image-based dead pixel processing method and device
CN114140336B (en) * 2021-10-08 2022-09-16 中国安全生产科学研究院 Infrared image-based dead pixel processing method and device

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