CN111163241A - Reversible information embedding method - Google Patents

Reversible information embedding method Download PDF

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CN111163241A
CN111163241A CN201911257070.4A CN201911257070A CN111163241A CN 111163241 A CN111163241 A CN 111163241A CN 201911257070 A CN201911257070 A CN 201911257070A CN 111163241 A CN111163241 A CN 111163241A
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information
data set
pixels
prediction
embedding
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CN111163241B (en
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常杰
朱国普
钱静
李应灿
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Shenzhen Institute of Advanced Technology of CAS
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
    • H04N1/32—Circuits or arrangements for control or supervision between transmitter and receiver or between image input and image output device, e.g. between a still-image camera and its memory or between a still-image camera and a printer device
    • H04N1/32101—Display, printing, storage or transmission of additional information, e.g. ID code, date and time or title
    • H04N1/32144—Display, printing, storage or transmission of additional information, e.g. ID code, date and time or title embedded in the image data, i.e. enclosed or integrated in the image, e.g. watermark, super-imposed logo or stamp
    • H04N1/32149—Methods relating to embedding, encoding, decoding, detection or retrieval operations
    • H04N1/32347—Reversible embedding, i.e. lossless, invertible, erasable, removable or distorsion-free embedding
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
    • H04N1/32—Circuits or arrangements for control or supervision between transmitter and receiver or between image input and image output device, e.g. between a still-image camera and its memory or between a still-image camera and a printer device
    • H04N1/32101—Display, printing, storage or transmission of additional information, e.g. ID code, date and time or title
    • H04N1/32144—Display, printing, storage or transmission of additional information, e.g. ID code, date and time or title embedded in the image data, i.e. enclosed or integrated in the image, e.g. watermark, super-imposed logo or stamp
    • H04N1/32149—Methods relating to embedding, encoding, decoding, detection or retrieval operations
    • H04N1/32203—Spatial or amplitude domain methods
    • H04N1/32229—Spatial or amplitude domain methods with selective or adaptive application of the additional information, e.g. in selected regions of the image

Abstract

The application belongs to the technical field of image processing, and particularly relates to a reversible information embedding method. The current reversible information embedding method realizes image pixel prediction based on the correlation of the distance between pixels. And realizing the prediction of the target pixel according to the characteristic that the target pixel has larger correlation with the pixel close to the target pixel. This prediction method is affected by distance and the resulting prediction accuracy is less than ideal. The method is based on a diamond prediction method, namely, the target pixels are predicted by using the average value of the target peripheral pixels, and then the original pixels are sequenced according to the sequence of the predicted values from small to large to produce a one-dimensional sequence. And finally, predicting the target pixel by utilizing the sequenced adjacent pixels, and realizing information embedding of the image based on a prediction error expansion method. Pixels with the same prediction value have larger correlation. And on the basis, the pixel prediction is realized to obtain higher prediction accuracy.

Description

Reversible information embedding method
Technical Field
The application belongs to the technical field of image processing, and particularly relates to a reversible information embedding method.
Background
In recent years, with the rapid development of internet technology and the popularization of digital devices such as mobile phones and computers, digital multimedia including images, texts, videos, audios and the like as information carriers are gradually recognized and accepted by the public. But at the same time, the multimedia information is easy to be maliciously tampered, copied and spread by illegal persons, and the interests of property owners are seriously damaged, so that the implementation of information security is beyond the limits of delay. The traditional encryption technology is to protect the content during the transmission of data by a sender, but after the data is received and decrypted, the data is very likely to be illegally copied and falsified. The information hiding technology is developed according to the defects in copyright protection and information security of the traditional cryptography. Reversible information hiding is an important method for information hiding. Reversible information hiding refers to a special method that an embedded carrier can completely recover after information is embedded in an image embedded in the carrier. Compared with the traditional information hiding method, reversible information hiding has stricter requirements on lossless recovery of an embedded carrier, is generally used for distortion-free protection of important images, and has important application value in military images and medical images.
The current reversible information embedding method realizes image pixel prediction based on the correlation of the distance between pixels. And realizing the prediction of the target pixel according to the characteristic that the target pixel has larger correlation with the pixel close to the target pixel. This prediction method is affected by distance and the resulting prediction accuracy is less than ideal.
Disclosure of Invention
1. Technical problem to be solved
The current reversible information embedding-based method realizes image pixel prediction based on the correlation of the distance between pixels. And realizing the prediction of the target pixel according to the characteristic that the target pixel has larger correlation with the pixel close to the target pixel. The method has the advantages that the prediction accuracy is not ideal due to the fact that the prediction method is influenced by the distance, and the reversible information embedding method is provided.
2. Technical scheme
In order to achieve the above object, the present application provides a reversible information embedding method, comprising the steps of:
step 1: acquiring an image, and dividing the image into a first data set and a second data set, wherein the first data set and the second data set are arranged alternately;
step 2: predicting a target pixel in the first data set to obtain a predicted value;
and step 3: calculating the complexity of the target pixel;
and 4, step 4: rearranging original pixels and complexity based on the sequence of the predicted values from small to large, setting a judgment threshold T of the image complexity, and selecting pixels with the complexity smaller than T from the sorted pixels for prediction;
and 5: embedding information when the error value is 0, and recording the position of finishing embedding after embedding half of the information;
step 6: and repeating the step 2-5, embedding the other half of information into the second data set, and recording the position of finishing embedding.
Another embodiment provided by the present application is: the second data set is processed in the same way as the first data set in the step 1.
Another embodiment provided by the present application is: the predicted value in the step 2 is
Figure BDA0002310565600000021
Figure BDA0002310565600000022
Where floor (x) denotes that x is rounded down, and a, b, c, d are the four pixel values nearest to the target pixel P.
Another embodiment provided by the present application is: the complexity in said step 3 is NL,
NL＝|a-d|+|b-c|+|a+c-b-d|+|c+d-a-b|。
another embodiment provided by the present application is: in the step 4, the prediction is to perform smooth movement on the screened pixels according to the window size of 1 × n (n ∈ (3,20), so as to realize the prediction of the pixels.
Another embodiment provided by the present application is: the prediction error
Figure BDA0002310565600000023
Figure BDA0002310565600000024
Is a predicted value.
Another embodiment provided by the present application is: in the step 5, the embedded information is i, i is 0 or 1, and the prediction error after the information is embedded is e'.
Another embodiment provided by the present application is: further comprising extracting information, said information extraction processing said second data set first.
Another embodiment provided by the present application is: the method further comprises the steps of:
and 7: predicting a target pixel in the second data set to obtain a predicted value, and calculating the complexity of the target pixel;
and 8: sorting original pixels according to the sequence of the predicted values from small to large, and screening out pixels with complexity < T for information extraction;
and step 9: and 7, repeating the steps 7 and 8, finishing the extraction of the first data set information, and finally restoring to the original image.
Another embodiment provided by the present application is: and in the step 8, the information extraction is to extract the information in the screened pixels in a reverse order according to the end position of the second data set.
3. Advantageous effects
Compared with the prior art, the reversible information embedding method has the advantages that:
according to the reversible information embedding method, the pixel prediction is realized by spanning the correlation of the distance between the pixels, and the prediction accuracy is improved, so that the embedding capacity is improved, and the distortion degree is reduced.
According to the reversible information embedding method, the pixels with the same predicted value have larger correlation. And on the basis, the pixel prediction is realized to obtain higher prediction accuracy.
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FIG. 1 is a schematic representation of the image obtained in step 1 of the present application;
FIG. 2 is a schematic diagram of the pixel prediction process of the present application;
FIG. 3 is a schematic diagram of the pixel prediction reverse order process of the present application;
figure 4 is a graph comparing the results of the barbarbara test of the present application with the prior art.
Detailed Description
Hereinafter, specific embodiments of the present application will be described in detail with reference to the accompanying drawings, and it will be apparent to those skilled in the art from this detailed description that the present application can be practiced. Features from different embodiments may be combined to yield new embodiments, or certain features may be substituted for certain embodiments to yield yet further preferred embodiments, without departing from the principles of the present application.
Reversible information embedding method based on prediction error expansion
According to the correlation of adjacent pixels, the adjacent pixels are adopted to predict the current pixel x to obtain a predicted value
Figure BDA0002310565600000031
The prediction error p is then:
Figure BDA0002310565600000032
when the prediction error is 0, the embedded information i, i is 0 or 1, and the other prediction error values are shifted, specifically:
Figure BDA0002310565600000033
the pixels after embedding the information are:
Figure BDA0002310565600000034
extracting the embedded information i as follows:
Figure BDA0002310565600000035
restore original pixel values:
Figure BDA0002310565600000036
thus, the embedding and extraction of information can be realized, and the original image can be restored.
Referring to fig. 1 to 4, the present application provides a reversible information embedding method, including the steps of:
step 1: acquiring an image, and dividing the image into a first data set and a second data set, wherein the first data set and the second data set are arranged alternately;
step 2: predicting a target pixel in the first data set to obtain a predicted value;
and step 3: calculating the complexity of the target pixel;
and 4, step 4: rearranging original pixels and complexity based on the sequence of the predicted values from small to large, wherein the arranged pixels are one-dimensional vectors; selecting pixels with complexity smaller than T from the sorted pixels for prediction;
and 5: embedding information when the error value is 0, and recording the position of finishing embedding after embedding half of the information;
step 6: and repeating the step 2-5, embedding the other half of information into the second data set, and recording the position of finishing embedding.
The image is divided into non-overlapping blue and white two-part datasets in a checkerboard fashion as shown in figure 1. The processing method of the two data sets of blue and white is the same, and the gray data set is taken as an example here to describe the whole embedding process. The embedded information is divided equally into two parts, one part being embedded in the gray dataset and the other part being embedded in the white dataset.
Further, the second data set in step 1 is processed in the same way as the first data set.
Further, the predicted value in the step 2 is
Figure BDA0002310565600000041
Figure BDA0002310565600000042
Where floor (x) denotes that x is rounded down.
Further, the complexity in step 3 is NL,
NL＝|a-d|+|b-c|+|a+c-b-d|+|c+d-a-b|。
further, in the step 4, the prediction is performed by performing smooth motion on the filtered pixels according to the window size of 1 × n (n ∈ (3,20)), so as to implement the prediction of the pixels, and the smooth motion is performed on the filtered pixels according to the window size of 1 × n (n ∈ (3,20) —. fig. 2 is a window with n ═ 5, the pixel p1 uses 5 pixels in the window 1 for prediction, the pixel p2 uses 5 pixels in the window 2 for prediction, and so on, so as to implement the prediction of other pixels.
Further, the prediction error
Figure BDA0002310565600000043
Figure BDA0002310565600000044
Is a predicted value.
Let the predicted value be
Figure BDA0002310565600000045
Then
Figure BDA0002310565600000046
Is composed of
Case 1: max (C) ≠ min (C)
Figure BDA0002310565600000047
Case 2: max (C) ═ min (C)
Figure BDA0002310565600000048
Prediction error
Figure BDA0002310565600000049
In step 5, the embedded information is i, i is 0 or 1, and the prediction error after the information is embedded is e'.
The method comprises the following specific steps;
case 1: max (C) ≠ min (C)
Figure BDA0002310565600000051
Case 2: max (C) ═ min (C)
Figure BDA0002310565600000052
The pixel p' after embedding the information is:
Figure BDA0002310565600000053
after half of the information is embedded, the end-location 1 of the end of the embedding is recorded.
And (3) repeating the steps 2-5 for a new image formed by the modified gray data set and the unmodified white data set, embedding the other half of information in the white data set, and recording the end embedding position end _ similarity 2.
Further, the method further comprises extracting information, and the information extraction is used for processing the second data set.
The image is divided into non-overlapping blue and white two-part datasets in a checkerboard fashion as shown in figure 1. The process of extracting information of the blue and white data sets is identical, and only the sequence is changed slightly. In the information embedding process, information is embedded in the gray data set and then embedded in the white data set. Then in extracting the information, it is necessary to first extract the information in the white data set and recover the pixels in the white data set. On the basis, the information in the gray data set is extracted again to recover the pixels in the gray data. Here, white data set extraction information is taken as an example.
Further, the method comprises the following steps:
and 7: predicting a target pixel in the second data set to obtain a predicted value, and calculating the complexity of the target pixel;
and 8: sorting original pixels according to the sequence of the predicted values from small to large, and screening out pixels with complexity < T for information extraction;
and step 9: and 7, repeating the steps 7 and 8, finishing the extraction of the first data set information, and finally restoring to the original image.
Further, the information extraction in step 8 is to extract the information in the filtered pixels in a reverse order according to the end position of the second data set.
Let the pixel in the white data set be X and the predicted value be
Figure BDA0002310565600000054
The prediction of the pixel X in the white pixel set is realized according to the prediction method of the embedded information, and the image complexity of the pixel X is calculated according to the complexity when the information is embedded.
And sequencing the original pixels according to the sequence of the predicted values from small to large, and screening out the pixels with NL < T for information extraction. The information in the filtered pixel s' is extracted in reverse order according to the end position end _ proximity 2.
Extracting invertible information
If NL < T
Case 1: max (C) ≠ min (C)
Figure BDA0002310565600000061
The original pixel value s is
Figure BDA0002310565600000062
Case 2: max (C) ═ min (C)
Figure BDA0002310565600000063
The original pixel value s is
Figure BDA0002310565600000064
The method adopts different embedding methods instead of one embedding method aiming at different image complexity, spans the obstacle of the distance between pixels, and realizes accurate prediction of the target pixel by utilizing the characteristic that the pixels with the same or similar predicted values have larger correlation. Compared with the prior art, the method improves the embedding capacity and effectively reduces the distortion degree of the image.
Specific experimental procedures and experimental results are given herein.
The data test set was derived from the SIPI data set, where standard test images Lena, Baboon, Barbara, Peppers, etc. were obtained with an image size of 512 x 512. The Peak Signal Noise Ratio (PSNR) value was used to measure the experimental effect.
The PSNR value calculation method comprises the following steps:
PSNR＝10*log10(2552/MSE)
Figure BDA0002310565600000065
Xi,j，X′i,jrepresenting pixels before and after embedding information.
Different determination thresholds are set for different images. Figure 4 is a graph comparing the results of the barbarbara test of the present application with the prior art.
Through comparison of fig. 4, it is found that the method proposed by the present application is superior to the existing research method.
The method is based on a diamond prediction method, namely, the target pixels are predicted by using the average value of the target peripheral pixels, and then the original pixels are sequenced according to the sequence of the predicted values from small to large to produce a one-dimensional sequence. And finally, predicting the target pixel by utilizing the sequenced adjacent pixels, and realizing information embedding of the image based on a prediction error expansion method. Pixels with the same prediction value have larger correlation. And on the basis, the pixel prediction is realized to obtain higher prediction accuracy.
Although the present application has been described above with reference to specific embodiments, those skilled in the art will recognize that many changes may be made in the configuration and details of the present application within the principles and scope of the present application. The scope of protection of the application is determined by the appended claims, and all changes that come within the meaning and range of equivalency of the technical features are intended to be embraced therein.

Claims (10)

1. A reversible information embedding method, characterized by: the method comprises the following steps:
step 1: acquiring an image, and dividing the image into a first data set and a second data set, wherein the first data set and the second data set are arranged alternately;
step 2: predicting a target pixel in the first data set to obtain a predicted value;
and step 3: calculating the complexity of the target pixel;
and 4, step 4: rearranging original pixels and complexity based on the sequence of the predicted values from small to large, setting a judgment threshold T of the image complexity, and selecting pixels with the complexity smaller than T from the sorted pixels for prediction;
and 5: embedding information when the error value is 0, and recording the position of finishing embedding after embedding half of the information;
step 6: and repeating the step 2-5, embedding the other half of information into the second data set, and recording the position of finishing embedding.
2. A reversible information embedding method as claimed in claim 1, characterized in that: the second data set is processed in the same way as the first data set in the step 1.
3. A reversible information embedding method as claimed in claim 1, characterized in that: the predicted value in the step 2 is
Figure FDA0002310565590000011
Figure FDA0002310565590000012
Where floor (x) denotes that x is rounded down, and a, b, c, d are the four pixel values nearest to the target pixel P.
4. A reversible information embedding method as claimed in claim 1, characterized in that: the complexity in said step 3 is NL,
NL＝|a-d|+|b-c|+|a+c-b-d|+|c+d-a-b|。
5. a reversible information embedding method as claimed in claim 1, characterized in that: in the step 4, the prediction is realized by performing smooth movement on the screened pixels according to the window size of 1 × n (n ∈ (3, 20)).
6. The reversible information embedding method of claim 5, wherein: the prediction error
Figure FDA0002310565590000013
Figure FDA0002310565590000014
Is a predicted value.
7. A reversible information embedding method as claimed in claim 1, characterized in that: in the step 5, the embedded information is i, i is 0 or 1, and the prediction error after the information is embedded is e'.
8. A reversible information embedding method as claimed in claim 1, characterized in that: further comprising extracting information, said information extraction processing said second data set first.
9. A reversible information embedding method as claimed in claim 8, wherein the information extraction process is characterized by: the method further comprises the steps of:
and 7: predicting a target pixel in the second data set to obtain a predicted value, and calculating the complexity of the target pixel;
and 8: sorting original pixels according to the sequence of the predicted values from small to large, and screening out pixels with complexity < T for information extraction;
and step 9: and 7, repeating the steps 7 and 8, finishing the extraction of the first data set information, and finally restoring to the original image.
10. The reversible information embedding method of claim 9, wherein: and in the step 8, the information extraction is to extract the information in the screened pixels in a reverse order according to the end position of the second data set.
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CN113099067A (en) * 2021-03-18 2021-07-09 西安交通大学 Reversible information hiding method and system based on pixel value sequencing prediction and diamond prediction

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