CN105554501B - Image acquisition and compression method and device - Google Patents
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Abstract
The invention relates to an image acquisition and compression method, belonging to the field of digital image acquisition and processing, comprising the following contents: on one hand, thumbnail data of an original image is obtained through down-sampling; on the other hand, the original image is subjected to sensing matrix sampling to obtain compressed data of the original image; according to a preset compression ratio, sequentially outputting the thumbnail data and compressed data obtained by compressing the sensing matrix to form a code stream for transmission or storage; after the code stream is stored and transmitted, the image can be previewed through the thumbnail data, and the original image is reconstructed through the compressed data acquired by combining the thumbnail data and the compressed sensing matrix. The invention effectively reduces the complexity of the traditional image compression method; the defect that the image can be viewed only through a reconstruction step based on conventional compressed sensing data is overcome, and the image reconstruction solution space is effectively reduced and the operation speed is improved by using the thumbnail data as a constraint condition; the requirement for network transmission of compressed data is reduced.
Description
Technical Field
The invention relates to a method and a device for collecting and compressing data, in particular to a method and a device for collecting and compressing high-efficiency images, belonging to the technical field of digital image collection and processing.
Background
In order to solve the storage and transmission challenges required for processing image mass data, the conventional method usually adopts compression technology, i.e. the limitation of the original data on storage space and transmission bandwidth is reduced by implementing a refined representation of the original digital signal. Current compression techniques are broadly divided into two categories: lossless compression and lossy compression. Lossless compression is, as the name implies, to recover the original digital signal without any distortion by using the refined representation after signal compression; lossy compression is to roughly recover the original signal by using a refined expression after signal compression, and although there is a certain error between the recovered signal and the original signal, the error is within an acceptable range in a specific application. Lossless compression is clearly attractive, but is often not suitable for applications requiring large compression ratios due to the limited compression ratios that it can provide. Data compression techniques are almost ubiquitous, for example, pictures we take, music we listen to, videos we enjoy, and even in the field of satellite-borne optical remote sensing, almost all photoelectric loads are equipped with special data compression units
Transform-domain coding is a popular data compression method, and usually transforms an original signal into an appropriate transform domain to exploit the sparse representation or compressible representation of the signal in the transform domain. The expression "sparsity" herein means that the original signal length is assumed to beNIn the transform domain the signal is onlyKA coefficient which is not zero, andK≪ Nby means of thisKThe non-zero coefficients may well represent the original input signal. By "compressible expression" is meant that the original signal passes well throughKA non-zero coefficient to approximate. The compression of the signal is realized by mining the sparsity expression of the signal, and the compression mode is adopted by many compression standards, such as JPEG, JPEG2000, H.264, MP3 and the like. The signal can be compressed because the signal itself has a large redundancy, whether it is a sound signal or an image signal. We review the conventional compression technique work process: firstly, sampling from an analog signal to a digital signal, wherein the analog signal contains a large amount of redundant data, then mining the sparsity of the signal through a transform domain, and finally realizing compression through a compression algorithm. The process actually comprises huge waste, a large amount of redundant data are collected firstly, then the redundant data are removed in the compression process, so that the redundant data are discarded at first, effective data are collected directly, the cost of the data collection process can be saved, the space can be saved, and the theory 'compressed sensing' adopted by the patent is introduced.
The English expression of "Compressed sensing" is Compressive sensing or CoNonreactive sampling, abbreviated CS. The word of simple compression is easy to understand, namely original redundant data is eliminated, and refined data which saves memory space is formed; the word "perception" alone is also well understood, i.e. signal sampling (the process of converting an analog signal into a digital signal). The translation of the "compressed sensing" is not easy to understand at first, but when we know the theory behind it, we can slowly understand its essence, i.e. the compression and sampling are combined into one, i.e. the sampling process is the compression process, and the compressed sensing sampled data is the compressed data itself. Compressed sensing theory indicates that sparse or finite-dimension signals with sparse representation can be reconstructed without distortion using linear, non-adaptive measurements much less than the number of nyquist samples. For a signalx∈R N ，xOnly containKA non-zero value. Suppose we acquire by a perceptual matrix phiMA linear measurement, i.e. we can describe this sampling process by the following mathematical modely=ΦxWhere Φ is a size ofM*NI.e. the measured values obtained by sampling. The matrix phi represents a projection operation for reducing the dimensionR N Mapping toR M In general, inK<M≪ NI.e. the number of columns of the matrix phi is much larger than the number of rows, this mathematical representation is a description of the standard compressed sensing framework. Once this theory has been proposed, it has attracted considerable attention in many areas such as information theory, signal/image processing, medical imaging, radio astronomy, pattern recognition, optical/radar imaging, channel coding, etc.
Although it is known that the acquisition and compression of the target signal can be efficiently completed by the compressed sensing method, the conventional data acquired based on compressed sensing must go through a reconstruction step to recover the original signal, and the optimization algorithm using the minimization of the L1 norm requires a lot of computation and is obviously not suitable for handheld devices, so that the camera based on the compressed sensing method is contrary to the convention of using a digital camera conventionally, because the shooting effect cannot be checked in real time through playback.
Disclosure of Invention
The invention aims to solve the problems that the traditional image acquisition and compression method is complex and low in efficiency, and the compressed sensing method is contrary to the traditional use habit of users, and provides an efficient image acquisition and compression method.
The purpose of the invention is realized by the following technical scheme:
an image acquisition and compression method comprising the following:
downsampling an original image acquired by an image array detector to acquire thumbnail data of the original image;
carrying out sensing matrix sampling on an original image acquired by an image array detector to obtain compressed data of the original image;
combining the thumbnail data and the compressed data as a code stream for storage or transmission;
previewing an image by using a thumbnail in the code stream;
and reconstructing the original image by combining the thumbnail data and the compressed data.
Preferably, the number of bytes of the thumbnail data and the number of bytes of the compressed data can be flexibly configured, on the premise of a fixed compression ratio, the number of bytes of the thumbnail data is determined according to the downsampling multiple, and the number of remaining bytes in the code stream required by the fixed compression ratio is filled with the compressed data.
Preferably, the sensing matrix is a matrix that satisfies or approximately satisfies a constrained isometry property (RIP) in compressed sensing theory.
Preferably, the matrix is a noise wave transform matrix (noise transform matrix), a Gaussian matrix (Gaussian matrix), a Bernoulli matrix (Bernoulli matrix), a Partial fourier transform matrix (Partial fourier transform matrix), or a Partial Hadamard matrix (Partial Hadamard matrix).
Preferably, in order to further increase the compression ratio, the code stream is encoded by lossless compression coding before being stored or transmitted.
Preferably, the lossless compression coding is Huffman coding (Huffman coding), Arithmetic coding (arithmetric coding), Run-Length coding (Run-Length coding), or adaptive dictionary coding (adaptive dictionary coding).
Preferably, the compressed data obtained by combining the thumbnail data and the sensing matrix is used as a code stream for storage or transmission, and the sequence is that the thumbnail data is stored or transmitted first and then the compressed data obtained by the sensing matrix is stored or transmitted.
Preferably, the original image is reconstructed by combining the thumbnail data and the compressed data, the method is implemented by adopting an optimization method of minimizing the L1 norm of the original image in a transform domain, and the purpose of reducing the solution space of the reconstructed image is achieved by taking constraint conditions that the downsampled data of the reconstructed image should be equal to or approximately equal to the thumbnail data of a receiving end or a storage end.
An image acquisition and compression device comprises an image detection module, a down-sampling module, a compressed sensing module, a compressed data output module and an image reconstruction module; the image detection module is respectively connected with the down-sampling module and the compressed sensing module, and the down-sampling module and the compressed sensing module are respectively connected with the compressed data output module;
the image detection module is used for acquiring an original image;
the down-sampling module is used for down-sampling the original image array output by the image detection module to obtain thumbnail data;
the compressed sensing module is used for sampling the original image output by the image detection module by a sensing matrix based on a compressed sensing theory to obtain compressed data;
the compressed data output module is used for combining the thumbnail data output by the down-sampling module and the compressed data output by the compressed sensing module as a code stream for storage or transmission and outputting the code stream;
the image reconstruction module is used for reconstructing an original image according to the thumbnail data and the compressed data.
Preferably, the compressed sensing module is implemented by using noise wave transformation, and the image reconstruction module is implemented by using an optimization method of minimizing the L1 norm in a transformation domain according to the original image.
Advantageous effects
Compared with the prior art, the efficient image acquisition and compression method provided by the invention has the advantages that on one hand, the down-sampling thumbnail of the original image is taken as compressed data; on the other hand, the compression of the original image is realized by compressing the sensing matrix. The advantages of this approach are: firstly, the problem that the imaging effect cannot be instantly reviewed under a compression perception theory-based imaging system is solved, because a reconstruction process based on L1 norm minimization is usually required by a method based on compression perception theory imaging, and the instant reviewing imaging effect is realized by a thumbnail inspection mode; secondly, the downsampling thumbnail of the original image is taken as a constraint condition, so that the quality of a reconstructed image minimized based on an L1 norm is greatly improved; third, the complexity of image acquisition and compression is greatly reduced.
The invention transmits the thumbnail with priority and then the compressed data acquired by compressed sensing in the coding or transmission sequence, and has the advantages that on one hand, a receiving end firstly sees the thumbnail and can select whether to need to reconstruct the original image with high resolution; on the other hand, according to the transmission mode, the receiving end is allowed to interrupt receiving under any compression ratio after receiving the thumbnail, the recovery of the complete image is not influenced, and only the difference in the quality of the reconstructed image is achieved, namely the more the compressed data is received, the better the quality of the reconstructed image is.
Drawings
FIG. 1 is a schematic flow chart of an image acquisition and compression method according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an image capturing and compressing apparatus according to an embodiment of the present invention.
Reference numerals: 101-image array detector; 102-a down-sampling module; 103-a compressed sensing module; 104-a compressed data output module; 105-image reconstruction module.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and examples.
Example 1
The following describes in detail an image acquisition and compression method provided by the present invention, taking an example that the quantization resolution of each pixel is 8 bits and the fixed compression ratio is 8, as shown in fig. 1, including the following contents:
1. and downsampling the original image acquired by the image array detector to obtain thumbnail data of the original image.
The downsampling process is illustrated by taking a CMOS image array detector with an array size of 2048 × 2048 and 256-fold downsampling as an example: the array is divided into a plurality of virtual small blocks according to 16 x 16, and only one pixel at the top left corner of each small block is read out from left to right and from top to bottom, so that 256 times down sampling of an original image is realized, and thumbnail data is obtained. According to this example, a person skilled in the art can block the array in the same manner and arbitrarily select pixels of each block to implement downsampling of a desired multiple; of course, the image array detector can also be directly selected from any existing array detector.
For this process, the expression can be formalized asy 1=DxWherein, the vectorxRepresenting the original image, the matrix D representing this down-sampling process, the vector, described abovey 1Representing the thumbnail image obtained after downsampling. Since the down-sampling is 256 times, the vector is outputy 1The number of bytes of (2) is 128 × 128= 16384. This step is essentially a read-out process and does not essentially change the quantized values of the pixels on the detector array.
2. And carrying out sensing matrix sampling on the original image acquired by the image array detector to obtain compressed data of the original image.
Since the fixed compression ratio is 8, the number of bytes of data finally output should be 2048 × 2048/8= 524288; since 16384 bytes of thumbnail data have been obtained by downsampling, only 524288-16384=507904 bytes of data can be output after perceptual matrix sampling. Through the process, the byte numbers of the thumbnail data and the compressed data can be flexibly configured, on the premise of a fixed compression ratio, the byte number of the thumbnail data is determined according to the down-sampling multiple, and the residual byte number in the code stream required by the fixed compression ratio is filled by the compressed data.
The matrix phi in the foregoing description is realized by combining with the prior art, and the sensing matrix phi represents a projection operation for reducing the dimensionR N Mapping toR M In general, inK<M≪ NI.e. the number of columns of the matrix Φ is much larger than the number of rows, commonly used sensing matrices include: gaussian matrices, bernoulli matrices and partial fourier transform matrices, but considering practical feasibility may limit their specific applications, for example, the feasibility of gaussian sampling matrices obeying gaussian distributions is hardly realized in hardware, on the one hand, we cannot store such sampling matrices absolutely obeying gaussian distributions in a memory; on the other hand, conventional memories cannot store very large sample matrices (when we need to be based on one sample number)M=10000Reconstructing an image with megapixelsN=1000000We require 10 gbytes of memory, which is not practical in practical applications, and the amount of computation required for the reconstruction process has not been taken into account, so it is not practical to use completely random measurements in practical applications). Therefore, the present embodiment implements the sensing matrix sampling with the noise wave transform example that is convenient for software and hardware implementation. Those skilled in the art will appreciate that any matrix that meets or approximately meets the constraint of the equidistant property in the compressed sensing theory can be applied according to the principles of the compressed sensing technique, such as noise wave transformation matrix (noise transform matrix), Gaussian matrix (Gaussian matrix), Bernoulli matrix (Bernoulli matrix), Partial Fourier transform matrix (Partial Fourier transform matrix), and Partial hadamard matrix (Partial hadamard matrix).
The process of sampling using the perceptual matrix is as follows:
can utilize the whole imageThe calculation process can be realized by referring to a real noise wave conversion method (r. Coifman, f. gewidth and y. Meyer, "noise elements", Applied and computer Harmonic Analysis 10, 2001), and the specific hardware implementation can adopt a Field-Programmable Array FPGA (Field-Programmable Gate Array) or other logic control devices to realize the conversion process in hardware. The transformation is a general transformation, no matter what kind of scene image is incident, the read data will become a signal like noise, and the transformed noise-like coefficient is the same as the original image size, namely 2048 × 2048. 507904 bytes are randomly selected from the transformed coefficients asy 2. Although the reading of the random position is randomly generated before the stream chip or before the hardware implementation, once the hardware is solidified, the random number sequence obtained from each run is necessarily the same as the random number function which is often used by us as long as the initial condition is the same. For example: the values output after each pass through the compressed sensing module are positions (1, 3), (71, 29), (41, 79), (32, 68) …. The noise wave coefficients at the positions are sequentially read out according to a fixed sequence to complete the transformation of the matrix phi and the acquisition of information, and the process can be formally expressed asy 2=ΦxWherein, the vectorxRepresenting the original image, matrix phi representing the above-mentioned perceptual matrix sampling process, vectory 1Representing compressed data acquired after sampling by the sensing matrix.
3. And combining the thumbnail data and the compressed data as a code stream for storage or transmission.
The thumbnail data and the compressed data are encoded and output to form a code stream for transmission or storage, and according to the use habit of a user, the embodiment adopts the sequential storage or transmission of the compressed data of the thumbnail data before and after, and the formalized expressionIs [ 2 ]y 1 , y 2]It can be understood as an approximately embedded form of coding. The benefits of outputting in this order are: on the one hand, the important measured valuesy 1The receiving end firstly sees the thumbnail and can select whether the original image needs to be reconstructed with high resolution or not, and the transmitting end does not need to transmit compressed data under the unnecessary conditiony 2The bandwidth can be saved; on the other hand, according to the transmission mode, the receiving end is allowed to interrupt the receiving of the compressed data under any compression ratio after receiving the thumbnail, the recovery of the complete image is not influenced, only the quality of the reconstructed image is different according to the size of the compressed data, namely, the quality of the reconstructed image is worse when the compression ratio is larger, and the reconstructed result is closer to the original input image when the compression ratio is smallerx。
In addition, if the compression ratio is further increased, a conventional lossless compression coding method, such as Huffman coding (Huffman coding), Arithmetic coding (arithmetric coding), Run-Length coding (Run-Length coding), adaptive dictionary coding (adaptive dictionary coding), etc., may be additionally embedded.
4. Previewing an image by using a thumbnail in the code stream; and reconstructing the original image by combining the thumbnail data and the compressed data.
Since the compressed data is not direct image data but data compressed by the sensing matrix, a reconstruction process is necessary to restore the original image. Because the number of sampling values obtained by sensing matrix sampling is obviously less than the number of pixels of an original image, namely the number of unknowns is far less than the number of equations, the reconstruction problem is a pathological problem, and an infinite number of solutions are often present to satisfy the equations. The most classical approach to solving this type of ill-conditioned problem is the Maximum A Posteriori (MAP), but often requires a prior probability model of the target signal as a constraint to find the solution closest to the target image from the infinite number of solutions. If other limiting conditions are added, the solution space can be further reduced, so that the thumbnail is taken as the result of down-sampling of the original image, and the constraint conditions play a significant role in the process of solving through an optimization algorithm.
Therefore, the image can be previewed directly by the thumbnail data after the code stream is decoded; reconstructing the original image by an optimization method of minimizing the L1 norm in the transform domain according to the original image, namely solving the following Cost Function (Cost Function):
Min{||W(x)||1} s.t.y 1=Dx；
y 2=Φx；
wherein s.t. represents a constraint condition, where the constraint conditions are 2, respectivelyy 1=DxAndy 2=Φxmeaning is based on compressed datay 2Reconstructed imagexShould be equal to the thumbnail data of the receiving end or the storage end; i1Represents L1A norm; the function W (.) represents an imagexSparseness can be embodied in the W transform domain, for example, W may be a wavelet transform domain because it is generally considered that a natural image can embody sparseness in the wavelet domain, but when W is selected, it is not limited thereto, but depends on the degree of understanding of image sparseness, i.e., a priori knowledge. The solution of the cost function can be implemented by using a conventional threshold-narrowing iterative algorithm (ISTA) well known to those skilled in the art.
Example 2
According to the image acquisition and compression method of embodiment 1, a set of image acquisition and compression device is realized, the structure of which is shown in fig. 2, and the image acquisition and compression device comprises: the image reconstruction method comprises an image detection module 101, a down-sampling module 102, a compressed sensing module 103, a compressed data output module 104 and an image reconstruction module 105; the image detection module is respectively connected with the down-sampling module and the compressed sensing module, and the down-sampling module and the compressed sensing module are respectively connected with the compressed data output module;
the image detection module is used for acquiring an original image, and the image detection module is realized by adopting the conventional image array detector in the embodiment;
the down-sampling module is used for down-sampling the original image output by the image detection module to obtain thumbnail data;
the compressed sensing module is used for sampling a sensing matrix based on a compressed sensing theory on an original image output by the image detection module to obtain compressed data, and the compressed data is realized by adopting the noise wave transformation in the image acquisition and compression method in embodiment 1;
the compressed data output module is used for combining the thumbnail data output by the down-sampling module and the compressed data output by the compressed sensing module as a code stream for storage or transmission and outputting the code stream, and in the embodiment, the code stream is generated by adopting the sequence of compressing the data after the thumbnail data in the image acquisition and compression method in the embodiment 1;
the image reconstruction module is configured to reconstruct an original image according to the thumbnail data and the compressed data, and this embodiment is implemented by using the optimization method of minimizing the norm of L1 in the transform domain of the original image in the image acquisition and compression method described in embodiment 1.
In summary, the present invention can satisfy the requirement of the handheld device user for checking the shooting effect through the thumbnail by adopting the method of combining the down-sampling and the compressed sensing model. Moreover, the thumbnail also plays a role of a constraint condition when an original image is reconstructed by utilizing L1 norm minimization in the patent, so the reconstruction method with the constraint condition added is far superior to the image quality based on the conventional compressed sensing reconstruction under the same compression ratio.
Moreover, the method of the invention also saves the complex processing process of finding important transformation coefficients and coding the important transformation coefficients in the conventional transformation coding, thereby having important practical significance: for example, for a satellite-borne optical imaging device, omitting the whole compression unit means saving a large amount of power consumption and volume, which is significant for space remote sensing. Meanwhile, the method is very suitable for the urgent requirements of low power consumption and portability of civil cameras. One problem that has plagued manufacturers of hand-held cameras or cell phone cameras is the power consumption of digital cameras. For example, the peak total power consumption of the ADV202, a JPEG2000 compression coding chip, produced by ADI chip corporation in the united states, can reach approximately 0.9 watts, which is a substantial burden for today's more and more popular handheld devices. Therefore, the power consumption of the handheld device can be reduced and the battery working time of the handheld device can be prolonged by the method, and the size and the weight of the handheld device can be reduced.
The above detailed description is intended to illustrate the objects, aspects and advantages of the present invention, and it should be understood that the above detailed description is only exemplary of the present invention and is not intended to limit the scope of the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. An image acquisition and compression method, characterized by comprising the following:
downsampling an original image acquired by an image array detector to acquire thumbnail data of the original image;
carrying out sensing matrix sampling on an original image acquired by an image array detector to obtain compressed data of the original image;
combining the thumbnail data and the compressed data as a code stream for storage or transmission;
previewing an image by using a thumbnail in the code stream;
and reconstructing the original image by combining the thumbnail data and the compressed data, and down-sampling the image reconstructed based on the compressed data into the thumbnail data.
2. The image acquisition and compression method according to claim 1, wherein the number of bytes of the thumbnail data and the number of bytes of the compressed data can be flexibly configured, and on the premise of a fixed compression ratio, the number of bytes of the thumbnail data is determined according to a downsampling multiple, and the number of remaining bytes in the code stream required by the fixed compression ratio is filled with the compressed data.
3. The method of claim 1, wherein the perceptual matrix is a matrix that satisfies or approximately satisfies a constrained equidistant property in a compressed sensing theory.
4. The image acquisition and compression method of claim 3, wherein the matrix is a noise wave transformation matrix, a Gaussian matrix, a Bernoulli matrix, a partial Hadamard matrix, or a partial Fourier transform matrix.
5. The image acquisition and compression method of claim 1, wherein, to further increase the compression ratio, the code stream is encoded using lossless compression coding before being stored or transmitted.
6. The image acquisition and compression method of claim 5, wherein the lossless compression coding is Huffman coding, arithmetic coding, run-length coding, or adaptive dictionary coding.
7. The image acquisition and compression method of claim 1, wherein the compressed data obtained by combining the thumbnail data and the perceptual matrix is used as a code stream for storage or transmission, and the sequence is that the thumbnail data is stored or transmitted first and then the compressed data is stored or transmitted.
8. The image acquisition and compression method as claimed in claim 1, wherein the original image is reconstructed by combining the thumbnail data and the compressed data, the method is implemented by using an optimization method of minimizing the norm of L1 in the transform domain of the original image, and the down-sampling of the reconstructed image should be equal to or approximately equal to the thumbnail data of the receiving end or the storage end as a constraint condition, so as to achieve the purpose of reducing the solution space of the reconstructed image.
9. An image acquisition and compression device is characterized by comprising an image detection module, a down-sampling module, a compressed sensing module, a compressed data output module and an image reconstruction module; the image detection module is respectively connected with the down-sampling module and the compressed sensing module, and the down-sampling module and the compressed sensing module are respectively connected with the compressed data output module;
the image detection module is used for acquiring an original image;
the down-sampling module is used for down-sampling the original image array output by the image detection module to obtain thumbnail data;
the compressed sensing module is used for sampling the original image output by the image detection module by a sensing matrix based on a compressed sensing theory to obtain compressed data;
the compressed data output module is used for combining the thumbnail data output by the down-sampling module and the compressed data output by the compressed sensing module as a code stream for storage or transmission and outputting the code stream;
the image reconstruction module is used for reconstructing an original image according to the thumbnail data and the compressed data, and down-sampling the image reconstructed based on the compressed data into the thumbnail data.
10. The image acquisition and compression device of claim 9, wherein the compressed sensing module is implemented by using noise wave transformation, and the image reconstruction module is implemented by using an optimization method of minimizing the L1 norm of the original image in the transformed domain.
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