CN107222749A - A kind of chaos code constructing method for wireless video transmission - Google Patents
A kind of chaos code constructing method for wireless video transmission Download PDFInfo
- Publication number
- CN107222749A CN107222749A CN201710474738.5A CN201710474738A CN107222749A CN 107222749 A CN107222749 A CN 107222749A CN 201710474738 A CN201710474738 A CN 201710474738A CN 107222749 A CN107222749 A CN 107222749A
- Authority
- CN
- China
- Prior art keywords
- mrow
- mtd
- real number
- symbol sequence
- number symbol
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 230000005540 biological transmission Effects 0.000 title claims abstract description 30
- 238000000034 method Methods 0.000 title claims abstract description 15
- 230000000739 chaotic effect Effects 0.000 claims abstract description 58
- 239000011159 matrix material Substances 0.000 claims abstract description 24
- 238000013507 mapping Methods 0.000 claims description 43
- 102100037812 Medium-wave-sensitive opsin 1 Human genes 0.000 claims description 17
- 238000010276 construction Methods 0.000 claims description 8
- 238000004364 calculation method Methods 0.000 claims description 6
- 101100517651 Caenorhabditis elegans num-1 gene Proteins 0.000 claims description 3
- 108010003272 Hyaluronate lyase Proteins 0.000 claims description 2
- 238000009826 distribution Methods 0.000 abstract description 10
- 238000006243 chemical reaction Methods 0.000 abstract 1
- 230000035945 sensitivity Effects 0.000 abstract 1
- 238000004088 simulation Methods 0.000 abstract 1
- 238000005728 strengthening Methods 0.000 abstract 1
- 238000010586 diagram Methods 0.000 description 4
- 108010076504 Protein Sorting Signals Proteins 0.000 description 3
- 238000012937 correction Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 241000282461 Canis lupus Species 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000002123 temporal effect Effects 0.000 description 2
- 239000000654 additive Substances 0.000 description 1
- 230000000996 additive effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000012886 linear function Methods 0.000 description 1
- 238000013139 quantization Methods 0.000 description 1
- 239000004576 sand Substances 0.000 description 1
- 238000009827 uniform distribution Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
- H04N19/169—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding
- H04N19/177—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being a group of pictures [GOP]
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/60—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using transform coding
- H04N19/625—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using transform coding using discrete cosine transform [DCT]
-
- 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
- H04N21/238—Interfacing the downstream path of the transmission network, e.g. adapting the transmission rate of a video stream to network bandwidth; Processing of multiplex streams
- H04N21/2383—Channel coding or modulation of digital bit-stream, e.g. QPSK modulation
Landscapes
- Engineering & Computer Science (AREA)
- Multimedia (AREA)
- Signal Processing (AREA)
- Physics & Mathematics (AREA)
- Discrete Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Compression Or Coding Systems Of Tv Signals (AREA)
Abstract
The present invention relates to a kind of chaos code constructing method for wireless video transmission, comprise the following steps：S1, original video sequence is divided into multiple GOP, obtains each GOP DCT coefficient matrix, be the first real number symbol sebolic addressing by the DCT coefficient matrix conversion；S2, step S1 the first real number symbol sebolic addressing is mapped as by the second real number symbol sebolic addressing by the chaotic maps function of design；S3, the second real number symbol sebolic addressing obtained to step S2 carry out power distribution.Compared with prior art, the present invention makes full use of chaos system to the high sensitivity characteristic and the feature of extended distance of original state, simulation chaotically coding is applied in image/video transmission, with the robustness for improving transmission of video, the advantages of strengthening the reconstruction quality of transmission of video.
Description
Technical Field
The invention relates to the field of wireless video transmission, in particular to a chaotic code construction method for wireless video transmission.
Background
Nowadays, with the increasing popularity of mobile internet and the widespread use of smart terminals, the amount of network data is explosively increasing, especially with image video data. Recent reports of cisco visual network statistics show that mobile video will grow 8.7 times from 2016 to 2021, with a leader in the growth rate of all mobile applications. By 2021, mobile video will account for 78% of the total traffic of mobile data. On the other hand, the conventional image video communication system has low reliability, and mosaic or even stop playing of image video transmission occurs due to the bandwidth time variation of a wired network or the channel time variation of a wireless link, so that the bottleneck of the system performance is caused. There are several problems in the field of wireless video research: one is mobility, for example, when a user holds a mobile device, the position changes continuously, and therefore its channel changes continuously, and therefore the quality of the channel also changes continuously; for the broadcast transmission of wireless video, different users are in different places, and the channel also has diversity characteristics; thirdly, with the popularization of video application, the time resolution and the space resolution of the receiving end are different from the mobile phone to each personal computer. It is therefore desirable that the quality of the video transmission be achieved to achieve a relatively good video quality over a range of channel qualities, while at the same time the video quality is expected to support multiple resolutions, including both temporal and spatial resolutions.
In digital systems, channel coding is used to protect compressed bit streams, and similar requirements are placed on analog video transmission. In analog video transmission, quantization and entropy coding are not performed, real-valued transform coefficients are transmitted, and therefore, the conventional finite field channel coding is not applicable. The encoding in analog image video transmission is real number encoding, and one of the chaotic encoding is mainly researched.
Wolf and Marshall have each separately proposed concepts related to analog error correction coding as early as the 80's of the last century. It was named real number coding in Marshall's research work and analog coding in Wolf's research. Similar to the basic principle of digital error correction, a good performance analog code should effectively amplify euclidean distances by distance extension. Chen and Wornell find that the chaotic dynamic system has the excellent characteristic. It generates a signal sequence by iteratively calling a chaotic function. Chaotic functions are characterized by their fast divergence, the butterfly effect. This effect means that even very small differences in the initial input will quickly result in a significantly different signal sequence. From a distance spread perspective, this indicates that even at a pair of closely spaced points in the source space, its encoded sequence will yield a large distance. Therefore, they propose the first analog chaotic code, which is designed by directly using nonlinear and real-valued chaotic Tent map as channel coding. Liu also uses analog chaotic coding to directly code the pixels of an image, which is inefficient due to the large amount of spatial and temporal redundancy of the pixels, and this is not in line with the real scene of image video transmission. These above analog chaotic codes are designed based on the input source being subject to uniform distribution, however, this assumption is not applicable to images and videos.
Disclosure of Invention
The present invention is directed to overcome the above-mentioned drawbacks of the prior art and to provide a method for constructing a chaotic code for wireless video transmission.
The purpose of the invention can be realized by the following technical scheme:
a chaotic code construction method for wireless video transmission comprises the following steps:
s1, dividing an original video sequence into a plurality of GOPs, solving a DCT coefficient matrix of each GOP, and converting the DCT coefficient matrix into a first real number symbol sequence S;
s2, mapping the first real number symbol sequence S in the step S1 into a second real number symbol sequence through a designed chaotic mapping function, wherein the formula of the chaotic mapping function is as follows:
and S3, distributing power to the second real number symbol sequence obtained in the step S2.
The process of finding the DCT coefficient matrix for each GOP in step S1 specifically includes: respectively carrying out 3-dimensional DCT on each divided GOP to obtain a DCT coefficient matrix SL×W×GWhere L is the height of each frame of image of the video sequence, W is the width of each frame of image of the video sequence, and G is the number of frames of the video sequence within a GOP.
The step S1 of converting the DCT coefficient matrix into a first real number symbol sequence specifically includes: the DCT coefficient matrix SL×W×GThe elements in the sequence are taken out in line, and then the first real number symbol sequence s ═ s [ i ] arranged in a single line in sequence],i＝0,1,…,L×W×G-1}。
The second real symbol sequence obtained in step S2 is represented by x ═ x (i), i ═ 0,1, …, and L × W × G × n-1, and has a length of m × n, where m ═ L × W × G and n is a bandwidth spreading factor.
The step S3 specifically includes:
s31, recoding the second real number symbol sequence and converting the second real number symbol sequence into a two-dimensional real number matrix Xchunk_num×chunk_sizeWherein, chunk _ num represents the number of blocks in a GOP, and chunk _ size represents the number of elements in a block;
s32, using pjDenotes the power allocated to the jth block, pjThe calculation formula of (2) is as follows:
wherein, gjDenotes the power scaling factor, λ, of the jth blockjRepresents the variance of the jth block before analog encoding,the calculation formula of (2) is as follows:
where P denotes the total power, λj' denotes a variance of the j-th block expanded after analog coding, j is 0,1, …, chunk _ num-1.
Compared with the prior art, the invention has the following advantages:
1. compared with the traditional wireless video digital transmission method, the invention fully utilizes the self-adaptability in analog coding modulation, introduces the chaos function based on analog coding in the pseudo-analog transmission SoftCast, can resist channel noise in wireless transmission, improves the robustness of data transmission, and enhances the reconstruction quality of video transmission while improving the power utilization rate.
2. For analog channel coding of real-valued input, the transformed DCT coefficient is used as an input information source, the chaotic function based on analog coding is used for DCT code elements, and a signal sequence is generated by iteratively calling some predefined chaotic mapping functions.
3. The chaos mapping function can keep linear relation for signals with small to medium values, while large signals are folded back under the nonlinear characteristic of the mapping function, and for input signals similar to gauss, the output of codes still basically keeps similar statistical characteristics, power punishment is reduced, and compared with input power, output power is slightly increased.
4. The invention solves the problem of power punishment of a Gaussian-like information source aiming at a natural image in a chaotic analog coding scheme, can be applied to a Gaussian distributed video image transmission technology, and has strong practicability.
Drawings
FIG. 1 is a schematic diagram of the transmission of DCT coefficients of an original video sequence in a Gaussian channel by using the method of the present invention;
FIG. 2 is a functional image of a conventional Tent map chaotic code;
FIG. 3 is a functional image of conventional Mod map chaotic encoding;
FIG. 4 is an image of a chaotic mapping function proposed by the present invention;
FIG. 5 is a schematic diagram of MSE performance of uniformly distributed information sources under three chaotic mapping functions;
fig. 6 is a schematic diagram of MSE performance of DCT coefficients of a natural image under three chaotic mapping functions.
The figure is marked with: 1. chaotic coder, 2, power distribution, 3, LLSE decoder, 4, chaotic decoder.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
In order to apply analog coding to image video transmission, a chaotic code construction method for wireless video transmission is provided, specifically a chaotic mapping function aiming at a source with generalized Gaussian distribution or Laplace distribution is constructed, and the chaotic mapping function is used for keeping a linear relation for signals from small to medium values, while large signals are folded back under the nonlinear characteristic of the mapping function. Thus, for a gaussian-like input signal, the encoded output still substantially retains similar statistical properties; the output power is slightly increased compared to the input power. The chaotic mapping function considers the statistical characteristics of the signal source, the error correction capability of the mapping function and the mutual balance relationship of the power between the original signal and the check symbol. The chaos code construction method for wireless video transmission comprises the following steps:
s1, establishing a Gaussian-like distribution information source:
s11, dividing the original video sequence into a plurality of GOPs;
s12, solving a DCT coefficient matrix of each GOP: respectively carrying out 3-dimensional DCT on each divided GOP to obtain a DCT coefficient matrix SL×W×GWherein L is the height of each frame of image of the video sequence, W is the width of each frame of image of the video sequence, and G is the number of frames of the video sequence in a GOP;
s13, converting the DCT coefficient matrix into a first real symbol sequence: the DCT coefficient matrix SL×W×GThe elements in the sequence are taken out in line, and then the first real number symbol sequence s ═ s [ i ] arranged in a single line in sequence],i＝0,1,…,L×W×G-1}。
S2, mapping the first real number symbol sequence S in the step S1 into a second real number symbol sequence through a designed chaos mapping function, wherein the chaos mapping function has the formula:
as shown in fig. 4, the obtained second real symbol sequence is represented by x ═ { x (i), i ═ 0,1, …, and L × W × G × n-1}, and has a length of m × n, where m ═ L × W × G, n is a bandwidth extension factor, and the corresponding code rate is 1/n;
s3, performing power allocation on the second real symbol sequence obtained in step S2:
s31, recoding the second real number symbol sequence and converting into two-dimensional real number matrix Xchunk_num×chunk_sizeWhere chunk _ num indicates the number of blocks in a GOP, and chunk _ size indicates the number of elements in a block, this also means that the matrix Xchunk_num×chunk_sizeEach row in (a) represents a block component combining adjacent spatial DCTs together with all their parity symbols, each block sharing the same power scaling factor;
s32, using pjRepresenting the corresponding power, p, allocated to the jth blockjThe calculation formula of (2) is as follows:
wherein, gjThe power scaling factor representing the jth block is used to minimize the mean square error estimate under power-limited conditions, λjRepresents the variance of the jth block before analog encoding,the calculation formula of (2) is as follows:
where P denotes the total power, λj' denotes the variance of the jth block, extended after analog coding, calculated based on the DCT components of this block and their coded parity symbols in this block, j-0, 1, …, chunk _ num-1.
Fig. 1 is a schematic diagram showing transmission of DCT coefficients of an original video sequence in a gaussian channel according to the present invention, where a DCT coefficient matrix is encoded by a chaotic encoder 1 and then power distribution 2 is performed, Additive White Gaussian Noise (AWGN) is added to a signal received by a receiving end, and the DCT coefficient matrix is decoded by a chaotic decoder 4 after passing through a linear least mean square estimation LLSE decoder 3 to obtain the original video sequence. The correctness and the effectiveness of the constructed chaotic mapping function are verified, and the system performance is evaluated by mean square error estimation (MSE).
The received symbol stream is represented by z ═ { z [ i ], i ═ 0,1, …, m × n-1}, and is calculated as:
z[i]＝gi×x[i]+w′[i]
wherein i is 0,1, …, m × n-1,w[i]is a mean of 0 and a varianceThe channel noise of (1).
The receiving end signal is decoded by LLSE to obtain y [ i ], which can be rewritten in a scalar form as:
the MSE after the receiving end finishes decoding the LLSE can be expressed as:
wherein,representing the variance of the channel noise.
Comparing the proposed chaotic mapping function with the Tent map and Mod map of the two chaotic mapping functions which are the most advanced at present, the function images are respectively shown in fig. 2 and fig. 3. For fairness, the analog coded symbols of all three mapping functions are set to have the same average power, and they are compared to map the decoded MSE. Since the performance of the chaotic mapping function is quite sensitive to the probability distribution of the input information source, the MSE performance of the chaotic mapping function is evaluated as shown in fig. 5 and fig. 6, wherein each graph compares the performance of a chaotic mapping function tent (tenthalf) with an encoding rate of 1/2, a chaotic mapping function mod (mod Half), a Proposed chaotic mapping function (advanced Half), a chaotic mapping function mod (mod Third) with an encoding rate of 1/3, and a Proposed chaotic mapping function (advanced Third).
Fig. 5 shows the MSE performance of the uniformly distributed source under three chaotic mapping functions. Since the gain is mainly obtained by the slope of the piecewise linear function, the overall performance of all three chaotic mapping functions is close. At high signal-to-noise ratios, rate 1/3 analog coding outperformed rate 1/2 analog coding, but at low signal-to-noise ratios they were similar.
Fig. 6 shows MSE performance of DCT coefficients of a natural image under three chaotic mapping functions, and the MSE performance of the chaotic mapping function provided by the present application is superior to that of the other two chaotic mapping functions no matter in analog coding with 1/3 code rate or 1/2 code rate.
Average power of s and x is Ps and P respectivelyxIs represented by Ps/PxRepresenting a power penalty factor, PsAnd Px are respectively calculated as:
power penalty factor Ps/PxNot only with the chaotic mapping function, but also with the distribution of the analog signal. When estimating Tent map, Mod map, Baker's map and the power penalty of the four chaotic mapping functions proposed in the present application, three different distributed information sources are used, as shown in table 1. For a uniformly distributed source, all four chaotic functions are without power penalty. For Gaussian distributed information sources, the chaotic mapping function provided by the application has a power penalty of 3.6dB, and the other three mapping functions respectively generate power penalties of 5.5-8.5 dB. The difference of the sources of the GGD or laplacian distribution, i.e. the "DCT coefficients" in the table, is very significant, the proposed mapping functionOnly a 1dB power penalty, while the other three mapping functions will yield a power penalty of more than 40 dB.
TABLE 1 Power penalty coefficients for different chaotic mapping functions
| Chaotic function | Is uniformly distributed | Gaussian distribution | DCT coefficient |
| Tent map | 0.0 | 5.5 | 42 |
| Mod map | 0.0 | 8.5 | 45 |
| Baker’s map | 0.0 | 5.5 | 42 |
| Proposed map | 0.0 | 3.6 | 1 |
Compared with the effectiveness and superiority of other methods, the method has the advantages that the MSE quality of the signal at the receiving end is higher than those of other two chaotic functions Tent map and Mod map no matter under the conditions of high signal-to-noise ratio or low signal-to-noise ratio by applying the chaotic code construction method. Meanwhile, the method for transmitting the wireless video based on the analog coding overcomes the cliff effect in the traditional wireless video transmission, realizes the continuous image quality attenuation along with the change of the channel quality when the video is transmitted in a wireless channel, and simultaneously realizes the high-quality reconstruction of the video at a receiving end.
Claims (6)
1. A chaotic code construction method for wireless video transmission is characterized by comprising the following steps:
s1, dividing an original video sequence into a plurality of GOPs, solving a DCT coefficient matrix of each GOP, and converting the DCT coefficient matrix into a first real number symbol sequence S;
s2, mapping the first real number symbol sequence S in the step S1 into a second real number symbol sequence through a designed chaotic mapping function, wherein the formula of the chaotic mapping function is as follows:
<mrow> <mi>x</mi> <mo>=</mo> <mi>f</mi> <mrow> <mo>(</mo> <mi>s</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <mn>2</mn> <mi>s</mi> <mo>+</mo> <mn>1</mn> </mrow> </mtd> <mtd> <mrow> <mo>-</mo> <mn>0.5</mn> <mo><</mo> <mi>s</mi> <mo>&le;</mo> <mo>-</mo> <mn>0.25</mn> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mn>2</mn> <mi>s</mi> </mrow> </mtd> <mtd> <mrow> <mo>-</mo> <mn>0.25</mn> <mo><</mo> <mi>s</mi> <mo>&le;</mo> <mn>0.25</mn> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mn>2</mn> <mi>s</mi> <mo>-</mo> <mn>1</mn> </mrow> </mtd> <mtd> <mrow> <mn>0.25</mn> <mo><</mo> <mi>s</mi> <mo>&le;</mo> <mn>0.5</mn> </mrow> </mtd> </mtr> </mtable> </mfenced> </mrow>
and S3, distributing power to the second real number symbol sequence obtained in the step S2.
2. The method of claim 1, wherein the step S1 of finding the DCT coefficient matrix for each GOP specifically comprises: respectively carrying out 3-dimensional DCT on each divided GOP to obtain a DCT coefficient matrix SL×W×GWhere L is the height of each frame of image of the video sequence, W is the width of each frame of image of the video sequence, and G is the number of frames of the video sequence within a GOP.
3. The method of claim 2, wherein the step S1 of converting the DCT coefficient matrix into a first real symbol sequence specifically comprises: the DCT coefficient matrix SL×W×GThe elements in the sequence are taken out in line, and then the first real number symbol sequence s ═ s [ i ] arranged in a single line in sequence],i＝0,1,…,L×W×G-1}。
4. The method of claim 2, wherein the second real symbol sequence obtained in step S2 is represented by x { x (i), i ═ 0,1, …, L × W × G × n-1}, and has a length of m × n, where m ═ L × W × G, and n is a bandwidth spreading factor.
5. The method of claim 1, wherein the step S3 specifically includes:
s31, recoding the second real number symbol sequence and converting the second real number symbol sequence into a two-dimensional real number matrix Xchunk_num×chunk_sizeWherein, chunk _ num represents the number of blocks in a GOP, and chunk _ size represents the number of elements in a block;
s32, using pjDenotes the power allocated to the jth block, pjThe calculation formula of (2) is as follows:
<mrow> <msub> <mi>p</mi> <mi>j</mi> </msub> <mo>=</mo> <msubsup> <mi>g</mi> <mi>j</mi> <mn>2</mn> </msubsup> <msub> <mi>&lambda;</mi> <mi>j</mi> </msub> </mrow>
wherein, gjDenotes the power scaling factor, λ, of the jth blockjThe variance of the jth block before analog encoding is shown.
6. The method of claim 5, wherein the chaotic code construction method in step S32 is implementedThe calculation formula of (2) is as follows:
<mrow> <msubsup> <mi>g</mi> <mi>j</mi> <mn>2</mn> </msubsup> <mo>=</mo> <mfrac> <mi>P</mi> <mrow> <msqrt> <msubsup> <mi>&lambda;</mi> <mi>j</mi> <mo>&prime;</mo> </msubsup> </msqrt> <munderover> <mo>&Sigma;</mo> <mrow> <mi>k</mi> <mo>=</mo> <mn>0</mn> </mrow> <mrow> <mi>c</mi> <mi>h</mi> <mi>u</mi> <mi>n</mi> <mi>k</mi> <mo>_</mo> <mi>n</mi> <mi>u</mi> <mi>m</mi> <mo>-</mo> <mn>1</mn> </mrow> </munderover> <msqrt> <msubsup> <mi>&lambda;</mi> <mi>k</mi> <mo>&prime;</mo> </msubsup> </msqrt> </mrow> </mfrac> </mrow>
wherein P represents total power, λ'jDenotes the variance of the jth block, which is extended after analog coding, j is 0,1, …, chunk _ num-1.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710474738.5A CN107222749B (en) | 2017-06-21 | 2017-06-21 | A kind of chaos code constructing method for wireless video transmission |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710474738.5A CN107222749B (en) | 2017-06-21 | 2017-06-21 | A kind of chaos code constructing method for wireless video transmission |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107222749A true CN107222749A (en) | 2017-09-29 |
| CN107222749B CN107222749B (en) | 2019-10-18 |
Family
ID=59950465
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710474738.5A Active CN107222749B (en) | 2017-06-21 | 2017-06-21 | A kind of chaos code constructing method for wireless video transmission |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107222749B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108111255A (en) * | 2017-11-24 | 2018-06-01 | 同济大学 | Interpretation method based on maximum a posteriori probability in a kind of analog encoding |
| CN108494522A (en) * | 2018-01-25 | 2018-09-04 | 同济大学 | A kind of building method of numerical model analysis Constructing Chaotic Code |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110142302A1 (en) * | 2009-12-10 | 2011-06-16 | Complex System, Inc. | Chaotic Watermarking for a Digital Image |
| CN102123026A (en) * | 2011-04-12 | 2011-07-13 | 南开大学 | Chaos and hyperchaos based two-level video streaming media encryption method |
| CN102722856A (en) * | 2012-06-06 | 2012-10-10 | 北京联合大学 | Digital image encryption method of DCT domain based on three dimensional Lorenz chaotic mapping |
| CN104683811A (en) * | 2013-11-29 | 2015-06-03 | 中国科学院声学研究所 | Information hiding and extracting method based on integer DCT (Discrete Cosine Transformation) coefficient modulation |
| CN105871537A (en) * | 2016-06-16 | 2016-08-17 | 广东工业大学 | Video communication method and device based on chaotic encryption |
| CN105979268A (en) * | 2016-05-05 | 2016-09-28 | 北京智捷伟讯科技有限公司 | Safe information transmission method based on lossless watermark embedding and safe video hiding |
| CN106028175A (en) * | 2016-06-21 | 2016-10-12 | 中国农业大学 | Method and device for realizing zero watermark of video |
| CN106303756A (en) * | 2016-10-10 | 2017-01-04 | 中国农业大学 | A kind of method and device for video copyright protecting |
| CN106408621A (en) * | 2016-09-20 | 2017-02-15 | 山东科技大学 | Image compression encryption and decryption method based on Lyapunov exponents |
| CN106851436A (en) * | 2017-03-31 | 2017-06-13 | 青岛科技大学 | Video point shape Fragile Watermark Algorithm in DCT domain |
-
2017
- 2017-06-21 CN CN201710474738.5A patent/CN107222749B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110142302A1 (en) * | 2009-12-10 | 2011-06-16 | Complex System, Inc. | Chaotic Watermarking for a Digital Image |
| CN102123026A (en) * | 2011-04-12 | 2011-07-13 | 南开大学 | Chaos and hyperchaos based two-level video streaming media encryption method |
| CN102722856A (en) * | 2012-06-06 | 2012-10-10 | 北京联合大学 | Digital image encryption method of DCT domain based on three dimensional Lorenz chaotic mapping |
| CN104683811A (en) * | 2013-11-29 | 2015-06-03 | 中国科学院声学研究所 | Information hiding and extracting method based on integer DCT (Discrete Cosine Transformation) coefficient modulation |
| CN105979268A (en) * | 2016-05-05 | 2016-09-28 | 北京智捷伟讯科技有限公司 | Safe information transmission method based on lossless watermark embedding and safe video hiding |
| CN105871537A (en) * | 2016-06-16 | 2016-08-17 | 广东工业大学 | Video communication method and device based on chaotic encryption |
| CN106028175A (en) * | 2016-06-21 | 2016-10-12 | 中国农业大学 | Method and device for realizing zero watermark of video |
| CN106408621A (en) * | 2016-09-20 | 2017-02-15 | 山东科技大学 | Image compression encryption and decryption method based on Lyapunov exponents |
| CN106303756A (en) * | 2016-10-10 | 2017-01-04 | 中国农业大学 | A kind of method and device for video copyright protecting |
| CN106851436A (en) * | 2017-03-31 | 2017-06-13 | 青岛科技大学 | Video point shape Fragile Watermark Algorithm in DCT domain |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108111255A (en) * | 2017-11-24 | 2018-06-01 | 同济大学 | Interpretation method based on maximum a posteriori probability in a kind of analog encoding |
| CN108494522A (en) * | 2018-01-25 | 2018-09-04 | 同济大学 | A kind of building method of numerical model analysis Constructing Chaotic Code |
| CN108494522B (en) * | 2018-01-25 | 2020-12-08 | 同济大学 | Construction method of digital-analog mixed chaotic code |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107222749B (en) | 2019-10-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP2527874B2 (en) | Device for imparting robustness to local image loss to compressed image data. | |
| JP5687767B2 (en) | Method and apparatus for arbitrary resolution video encoding using compressed sampling measurements | |
| CN106803958B (en) | Digital-analog hybrid video transmission method based on superposition modulation coding | |
| Jiang et al. | Scalable video coding using compressive sensing | |
| KR101266667B1 (en) | Dual-mode compression of images and videos for reliable real-time transmission | |
| CN105357536B (en) | The soft method of multicasting of video based on residual distribution formula compressed sensing | |
| US10356404B1 (en) | Image processing using just-noticeable-difference thresholds | |
| Wu et al. | DAC-Mobi: Data-assisted communications of mobile images with cloud computing support | |
| CN107222749B (en) | A kind of chaos code constructing method for wireless video transmission | |
| Niu et al. | A hybrid wireless image transmission scheme with diffusion | |
| US8681860B2 (en) | Moving picture compression apparatus and method of controlling operation of same | |
| Lan et al. | A practical hybrid digital-analog scheme for wireless video transmission | |
| CN108111255A (en) | Interpretation method based on maximum a posteriori probability in a kind of analog encoding | |
| CN112085667A (en) | Deblocking effect removing method and device based on pseudo-analog video transmission | |
| CN108347615A (en) | A kind of spatial domain telescopic video transmission method solving screen resolution Heterogeneity | |
| Li et al. | Soft transmission of 3D video for low power and low complexity scenario | |
| Xu et al. | Optimization-Based Pseudo-Analog Transmission Scheme for Wireless Feature Map Transmission | |
| Wu et al. | DCSN-Cast: deep compressed sensing network for wireless video multicast | |
| Pattanaik et al. | A novel lossless image compression algorithm using arithmetic modulo operation | |
| CN114915773B (en) | Robust transmission method for minimizing overlapping distortion of 360-degree VR (virtual reality) video field | |
| Liu et al. | A Lossy Secret Color Image Sharing Scheme with Small Shadows and Error-resilient Capability. | |
| Yami et al. | Visual attention-driven wireless multicasting of images using adaptive compressed sensing | |
| CN106797481A (en) | Method for transmitting signals, device and equipment | |
| Senthamilselvan et al. | Wireless transmission based image quality analysis using uni-level Haar wavelet transform | |
| CN110099279B (en) | Method for adjusting lossy compression based on hardware |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |