CN110191345B - Incremental lossless compression method for separating foreground and background based on Huffman coding - Google Patents
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Abstract
The invention discloses a picture incremental lossless compression method based on foreground and background separation of Huffman coding. The invention shoots a batch of picture groups of target objects in the same scene through the acquisition device, performs differential incremental compression on a large number of pictures shot in the same scene by adopting a lossless compression technology based on Huffman coding, and makes the difference distribution more concentrated by separating the foreground from the background so as to obtain the shortest possible coding length, obtain the best lossless compression effect, reduce the occupation of storage space, and realize rapid decoding and restoration of the pictures for the subsequent upper layer of the pictures.
Description
Technical Field
The invention belongs to the field of image compression, and relates to an incremental lossless compression method based on Huffman coding.
Background
In order to meet the daily operation inspection requirements of power grid equipment, a transformer substation of a certain power supply bureau of the national power grid starts an intelligent inspection robot, the lapping surface of the power grid and an instrument are subjected to daily photographing, and the operation and maintenance efficiency is improved. The inspection robot inspects and shoots key equipment and a lap joint of the transformer substation along a set path in a working scene, and information such as reading of relevant instruments and temperature of the lap joint is identified. However, the robot has a large amount of data in daily life, including three categories of visible light photos, infrared photos and infrared data, the formats of the pictures mainly include jpg, bmp, png and the like, and the data generated by the robot is stored by a local common PC. The massive data provides a foundation for data mining of operation and maintenance of the power station, but the large-scale, heterogeneous and multi-source characteristics of the data also bring huge challenges to storage and use of the data, so that massive pictures need to be effectively compressed to relieve storage pressure, lossless decompression needs to be achieved to avoid information loss, and support is provided for subsequent use.
The Huffman coding is a lossless compression method with wide application, constructs a code word with the shortest average length of different character heads according to the occurrence probability of characters so as to realize the purpose of making the coding length of all the characters shortest, can effectively reduce the storage space of data, has fewer used limiting conditions and extremely strong universality, and is easy to realize through programming.
Disclosure of Invention
The invention aims to solve the problems of storage pressure and inconvenience in use caused by the mass of pictures generated by an intelligent inspection robot of a power grid transformer substation, a lossless compression technology based on Hoffman coding is adopted, a large number of pictures shot in the same scene are subjected to differential incremental compression, the difference distribution is more concentrated through a foreground and background separation mode, the coding length which is as short as possible is obtained, the optimal lossless compression effect is obtained, the occupation of storage space is reduced, the pictures can be quickly decoded and restored for subsequent upper layers of the pictures, the functions can be realized according to threshold values and differential base information for newly added pictures, and the invention has greater application value and economic significance.
The purpose of the invention is realized by the following technical scheme: a picture incremental lossless compression method based on foreground and background separation of Huffman coding comprises the following steps:
(1) the batch picture group X of the target object under the same scene is shot through the acquisition device and is stored in the form of an array, which is expressed as:
wherein, XjThe method is a three-dimensional matrix array storage form of the jth picture, and m is the total number of pictures shot by the acquisition device in the same scene;
the storage space S of m pictures is recorded as:
wherein S isjA storage space before the compression of the jth picture is obtained;
(2) when the compression processing of the picture is carried out, the original picture is divided into three dimensions according to the three components of R, G and B of the picture to be respectively processed and separately stored as Xr，Xg，XbThree arrays, represented as:
wherein, XrjArray stored for component R of jth picture, XgjArray stored for component G of jth picture, XbjAn array stored for the component B of the jth picture;
(3) randomly selecting Q pictures in a picture group shot in the same scene, taking the average value of pixel values of the same pixel point position of the Q pictures as the pixel value of the pixel point position of a base picture to obtain the base picture, and then respectively subtracting R, G and B components of all pictures in the scene from R, G and B components of the base picture to obtain a difference value array delta Xr，ΔXg，ΔXbWhich can be respectively expressed as:
wherein, Δ XriThe difference value array of the R component of the ith picture and the R component of the base is obtained; Δ XgiA difference value array of the component G of the ith picture and the component G of the substrate is obtained; Δ XbiA difference value array of the component B of the ith picture and the component B of the base is obtained;
(4) taking the iteration number k as 0, and determining according to the distribution range of the difference value array valueThreshold distribution interval [ epsilon ] of foreground and background separationmin,εmax]Selecting an initial threshold value epsilon for separating the foreground from the backgroundk＝εmin(e.g. selecting an initial threshold ε in the interval 10-200)k10); according to a threshold value epsilonkPixel difference value is greater than or equal to epsilonkIs stored as foreground part, less than epsilonkThe part of (2) is stored as a background part, so that the picture array is divided into a foreground part and a background part, and the foreground part and the background part are stored separately and are respectively marked as delta Xrq，ΔXrb，ΔXgq，ΔXgb，ΔXbq，ΔXbbExpressed as:
ΔXrq＝[ΔXrq1,ΔXrq2,...,ΔXrqm]T
ΔXrb＝[ΔXrb1,ΔXrb2,...,ΔXrbm]T
ΔXgq＝[ΔXgq1,ΔXgq2,...,ΔXgqm]T
ΔXgb＝[ΔXgb1,ΔXgb2,...,ΔXgbm]T
ΔXbq＝[ΔXbq1,ΔXbq2,...,ΔXbqm]T
ΔXbb＝[ΔXbb1,ΔXbb2,...,ΔXbbm]T
wherein, Δ XrqiA difference value array of the foreground red component and the substrate red component of the ith picture is obtained; Δ XrbiA difference value array of the background red component and the substrate red component of the ith picture is obtained; Δ XgqiA difference value array of the foreground green component and the substrate green component of the ith picture is obtained; Δ XgbiA difference value array of the background green component and the substrate green component of the ith picture is obtained; Δ XbqiA difference value array of the foreground blue component and the substrate blue component of the ith picture is obtained; Δ XbbiA difference value array of the background blue component and the substrate blue component of the ith picture is obtained;
(5) after obtaining the difference value arrays of R, G and B with concentrated distribution, adopting classical Huffman coding to pair deltaXrqi，ΔXrbi，ΔXgqi，ΔXgbi，ΔXbqi，ΔXbbiLossless compression storage is carried out to obtain average coding length
Expressed as:
wherein,
the average coding length of the difference array of the foreground red component and the background red component of the ith picture is,
the coding length, n, of the a-th pixel point of the difference array of the foreground red component and the basement red component of a picturerqiThe number of the difference array pixel points of the foreground red component and the background red component of the ith picture is the same as the number of the pixel points of the difference array pixel points of the foreground red component and the background red component of the ith picture, and the meanings of other symbols can be analogized, so that detailed description is avoided;
(6) after obtaining R, G and B three-dimensional average coding length of foreground and background, calculating lossless compression ratio C of R, G and B three components of ith pictureri，Cgi，CbiThe expression is as follows:
wherein n isriThe total number of R component pixel points of the ith picture is; n isgiThe total number of pixel points of the G component of the ith picture is; n isbiIs the total number of B component pixels of the ith picture, and nri＝ngi＝nbi＝ni/3, equal to the total number n of pixel points of the ith picturei1/3 of (1).
(7) Lossless compression ratio of ith picture is CiIt can be expressed as:
when the total number of the pictures to be compressed is n, the total lossless compression ratio is C0Comprises the following steps:
(8) when epsilonk＝εmaxThen, entering the step (9); when epsilonk＜εmaxWhen k is k +1, epsilonk＝εk-1And (5) taking the + delta epsilon and the delta epsilon as threshold step lengths (10 can be selected), and repeating the steps (4) to (7) to obtain a new total lossless compression ratio Ck；
(9) Selecting a corresponding threshold value when the total lossless compression ratio is minimum as a final threshold value of picture compression in the scene to perform lossless compression processing on the batch of pictures; after lossless compression is carried out on the picture i, pixel point difference values of a foreground and a background in three dimensions are stored as six sets of Huffman codes;
(10) when the picture i is restored, the foreground and background codes of all dimensions are restored into pixels before compression, the pixels are summed with the base pixels, and the original picture before lossless compression can be obtained by displaying the three dimensions together.
Furthermore, the method is applied to the intelligent inspection robot of a certain transformer substation to shoot batch image groups of the target object at fixed points along a fixed route in the same scene, and the images are compressed. Because the inspection robot carries out fixed-point shooting pictures along a fixed route, the shooting background (sky and the like) and the shooting foreground (telegraph pole and the like) are obviously different, and the difference distribution is more concentrated after the inspection robot is separated according to the threshold value than before the inspection robot is separated.
The invention has the beneficial effects that: the invention adopts lossless compression technology based on Huffman coding to perform differential incremental compression on a large number of pictures shot in the same scene, and the mode of separating the foreground from the background is to make the difference distribution more centralized so as to obtain the coding length as short as possible, obtain the best lossless compression effect, reduce the occupation of storage space, and realize rapid decoding and restoration of the pictures for the subsequent upper layer of the pictures.
Drawings
FIG. 1 is a flow chart of the incremental lossless compression method based on the foreground and background separation of Huffman coding of the present invention;
FIG. 2 is a visual image of an R component difference array of a certain image;
fig. 3 is a difference array visualization picture with foreground and background separation performed through a threshold value.
Detailed Description
The invention is further described below with reference to the accompanying drawings and specific embodiments. The following examples are only for illustrating the technical solutions of the present invention more clearly, and should not be taken as limiting the scope of the present invention.
In order to meet the requirement of intelligent operation and inspection, the investment of an inspection robot is continuously increased in the daily power transformation operation of a power company, and manual inspection is replaced to a certain extent. The robot patrols and examines and produces huge picture daily, and the storage technique that adopts at present is difficult to handle large-scale picture increment, therefore needs to invest in the storage equipment that a large amount of funds were used for expanding resources such as data, picture in the future. The research of obtaining pictures in the same scene shows that the shooting backgrounds are basically the same, the shooting foreground difference is relatively large, the common background (namely a base image) is extracted, the difference value between the pictures and the common background is calculated from each pixel, and the difference value is divided into blocks obviously.
The incremental lossless compression method for separating the foreground from the background based on the Huffman coding, which is provided by the invention, can be used for compressing a class of pictures with a common background, as shown in figure 1, and comprises the following steps:
(1) in the example, a certain transformer substation of the Wenzhou power grid is taken as an example, bmp format pictures (the size is 240 × 320) are selected to be subjected to lossless compression processing in a scene of wiring shooting of a robot inspection tower in one month, and after MATLAB is used for reading the pictures, one bmp picture is taken as an example to obtain a three-dimensional array of 240 × 320 × 3:
(2) when compression processing is carried out, an original picture is divided into R, G and B, three dimensions are respectively processed and separately stored as Xr，Xg，XbThree arrays, one each at 240 x 320:
(3) taking the collection of 10 pictures as an example, the storage space S before the compression of 10 pictures is recorded as (unit: bit):
(4) randomly taking 20 pictures from the shot pictures of the same scene, taking the average value of each pixel point as a substrate, and respectively subtracting the R, G and B components of other pictures in the scene from the R, G and B components of the substrate to obtain a difference value array delta Xr，ΔXg，ΔXbTake a picture to be compressed as an example, Δ XrThe visualization is as shown in fig. 2 below:
(5) selecting and determining the optimal threshold between 0 and 200 by taking 10 as step length according to the numerical distribution of the difference value arrayThe value of the compression ratio is minimized, and the difference value of the foreground and the background is separately stored and is respectively recorded as delta Xrq，ΔXrb，ΔXgqΔXgb，ΔXbq，ΔXbbHere, the threshold value is taken as 50, and Δ X is obtained by separating the foreground and the background of a picture to be compressed, for examplerq，ΔXrbThe visualization is shown in figure 3 below.
(6) After obtaining the difference value arrays of R, G and B with concentrated distribution, adopting classical Huffman coding to carry out delta Xrqi，ΔXrbi，ΔXgqi，ΔXgbi，ΔXbqi，ΔXbbiLossless compression storage is carried out, and average coding length can be obtained
The average coding length of one picture to be compressed is shown in table 1 below:
TABLE 1
(7) After obtaining R, G and B three-dimensional average coding length of foreground and background, calculating lossless compression ratio C of R, G and B three components of ith pictureri，Cgi，CbiFor example, for one picture to be compressed, the compression ratio of the R, G, and B components is shown in table 2 below:
TABLE 2
(8) The lossless compression ratio of the picture is C:
when the total number of pictures to be compressed is n equals to 10, the total lossless compression ratio C is:
(9) according to the corresponding decoding technology, after the picture to be decompressed is summed with the substrate, decompression can be realized, namely, the fast lossless restoration of batch compressed pictures is realized so as to support the use of a subsequent upper layer.
After the method is tested in a non-public large-scale test set provided by Wenzhou power grid company, compression ratios are selected according to different base images and separation threshold values, the range of lossless compression ratios is distributed in a closed interval [0.1764,0.1985], the compression effect is obvious, the storage space can be effectively saved, and the method has great economic significance and application value.
The above-described embodiments are intended to illustrate rather than to limit the invention, and any modifications and variations of the present invention are within the spirit of the invention and the scope of the appended claims.
Claims (2)
1. A picture incremental lossless compression method based on foreground and background separation of Huffman coding is characterized by comprising the following steps:
(1) the batch picture group X of the target object under the same scene is shot through the acquisition device and is stored in the form of an array, which is expressed as:
wherein, XjThe method is a three-dimensional matrix array storage form of the jth picture, and m is the total number of pictures shot by the acquisition device in the same scene;
the storage space S of m pictures is recorded as:
wherein S isjA storage space before the compression of the jth picture is obtained;
(2) when the compression processing of the picture is carried out, the original picture is divided into three dimensions according to the three components of R, G and B of the picture to be processed respectivelyIs divided and stored as Xr，Xg，XbThree arrays, represented as:
wherein, XrjArray stored for component R of jth picture, XgjArray stored for component G of jth picture, XbjAn array stored for the component B of the jth picture;
(3) randomly selecting Q pictures in a picture group shot in the same scene, taking the average value of pixel values of the same pixel point position of the Q pictures as the pixel value of the pixel point position of a base picture to obtain the base picture, and then respectively subtracting R, G and B components of all pictures in the scene from R, G and B components of the base picture to obtain a difference value array delta Xr，ΔXg，ΔXbWhich can be respectively expressed as:
wherein, Δ XriThe difference value array of the R component of the ith picture and the R component of the base is obtained; Δ XgiA difference value array of the component G of the ith picture and the component G of the substrate is obtained; Δ XbiA difference value array of the component B of the ith picture and the component B of the base is obtained;
(4) taking the iteration number k as 0, and determining a threshold distribution region [ epsilon ] of foreground and background separation according to the distribution range of the difference array valuesmin,εmax]Selecting an initial threshold value epsilon for separating the foreground from the backgroundk＝εmin(ii) a According to a threshold value epsilonkPixel difference value is greater than or equal to epsilonkIs stored as foreground part, less than epsilonkThe part of (2) is stored as a background part, so that the picture array is divided into a foreground part and a background part, and the foreground part and the background part are stored separately and are respectively marked as delta Xrq，ΔXrb，ΔXgq，ΔXgb，ΔXbq，ΔXbbExpressed as:
ΔXrq＝[ΔXrq1,ΔXrq2,...,ΔXrqm]T
ΔXrb＝[ΔXrb1,ΔXrb2,...,ΔXrbm]T
ΔXgq＝[ΔXgq1,ΔXgq2,...,ΔXgqm]T
ΔXgb＝[ΔXgb1,ΔXgb2,...,ΔXgbm]T
ΔXbq＝[ΔXbq1,ΔXbq2,...,ΔXbqm]T
ΔXbb＝[ΔXbb1,ΔXbb2,...,ΔXbbm]T
wherein, Δ XrqiA difference value array of the foreground red component and the substrate red component of the ith picture is obtained; Δ XrbiA difference value array of the background red component and the substrate red component of the ith picture is obtained; Δ XgqiA difference value array of the foreground green component and the substrate green component of the ith picture is obtained; Δ XgbiA difference value array of the background green component and the substrate green component of the ith picture is obtained; Δ XbqiA difference value array of the foreground blue component and the substrate blue component of the ith picture is obtained; Δ XbbiA difference value array of the background blue component and the substrate blue component of the ith picture is obtained;
(5) after obtaining the difference value arrays of R, G and B with concentrated distribution, adopting classical Huffman coding to carry out delta Xrqi，ΔXrbi，ΔXgqi，ΔXgbi，ΔXbqi，ΔXbbiLossless compression storage is carried out to obtain average coding length
Expressed as:
wherein,
the average coding length of the difference array of the foreground red component and the background red component of the ith picture is,
the coding length n of the a-th pixel point of the difference array of the foreground red component and the basement red component of the ith picturerqiThe number of pixel points is the difference value array of the foreground red component and the basement red component of the ith picture;
the average coding length of a difference value array of the background red component and the basement red component of the ith picture is obtained;
the average coding length of a difference value array of the foreground green component and the base green component of the ith picture is obtained;
the average coding length of a difference value array of the background green component and the base green component of the ith picture is obtained;
the average coding length of the difference value array of the foreground blue component and the background blue component of the ith picture is obtained;
the average coding length of the difference value array of the background blue component and the background blue component of the ith picture is obtained;
coding of a-th pixel point of a difference array of the foreground green component and the basement green component of the ith pictureThe length of the code is set by the code length,
the code length of the a-th pixel point of the difference array of the foreground blue component and the background blue component of the ith picture,
the code length of the a-th pixel point of the difference array of the background red component and the basement red component of the ith picture,
the code length of the a-th pixel point of the difference array of the background green component and the substrate green component of the ith picture,
the coding length of the a-th pixel point of the difference array of the background blue component and the background blue component of the ith picture is obtained; n isgqThe number of difference array pixel points of the foreground green component and the basement green component of the ith picture, nbqiThe number of pixel points of a difference array of the foreground blue component and the background blue component of the ith picture, nrbiThe number of pixel points of the difference array of the background red component and the basement red component of the ith picture, ngbiThe number of pixel points is the difference array of the background green component and the substrate green component of the ith picture, nbbi is the number of pixel points of the difference array of the background blue component and the substrate blue component of the ith picture;
(6) after obtaining R, G and B three-dimensional average coding length of foreground and background, calculating lossless compression ratio C of R, G and B three components of ith pictureri，Cgi，CbiThe expression is as follows:
wherein n isriThe total number of R component pixel points of the ith picture is; n isgiThe total number of pixel points of the G component of the ith picture is; n isbiIs the total number of B component pixels of the ith picture, and nri＝ngi＝nbi＝ni/3, equal to the total number n of pixel points of the ith picturei1/3 of (1);
(7) lossless compression ratio of ith picture is CiIt can be expressed as:
when the total number of the pictures to be compressed is n, the total lossless compression ratio is C0Comprises the following steps:
(8) when epsilonk＝εmaxThen, entering the step (9); when epsilonk＜εmaxWhen k is k +1, epsilonk＝εk-1And (5) repeating the steps (4) to (7) by taking the + delta epsilon and the delta epsilon as threshold step lengths to obtain a new total lossless compression ratio Ck；
(9) Selecting a corresponding threshold value when the total lossless compression ratio is minimum as a final threshold value of picture compression in the scene to perform lossless compression processing on the batch of pictures; after lossless compression is carried out on the picture i, pixel point difference values of a foreground and a background in three dimensions are stored as six sets of Huffman codes;
(10) and when the picture i is restored, the foreground and background codes of all dimensions are restored into pixels before compression, and after the pixels are summed with the base pixels, the three dimensions are displayed together to obtain the original picture before lossless compression.
2. The Huffman coding-based incremental lossless compression method for pictures with separated foreground and background is applied to batch picture groups of a target object shot by an intelligent inspection robot of a certain transformer substation along a fixed route at a fixed point in the same scene, and the pictures are compressed.
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