141results about How to "Improve refactoring quality" patented technology

The invention discloses a non-convex compressed sensing image reconstruction method based on a redundant dictionary and structure sparsity. A reconstruction process of the method includes: observing original image blocks; using a mutual neighboring technology for clustering observation vectors; using a genetic algorithm for finding optimal atom combinations in a dictionary direction for each class of observation vectors, and preserving species; after species expansion operation is executed on each image block, using a clonal selection algorithm for finding an optimal atom combination on scale and displacement in a determined direction for each image block; reconstructing each image block by the optimal atom combination; and piecing all the constructed image blocks in sequence to form an entire constructed image. Image structure sparsity prior and redundant dictionary direction features are fully utilized, the genetic algorithm is combined with the clonal selection algorithm, and the method is used as a nonlinear optimization reconstruction method to realize image reconstruction. The reconstructed image is good in visual effect, high in peak signal noise ratio and structural similarity, and the method can be used for non-convex compressed sensing reconstruction of image signals.

The invention relates to a hyperspectral image super-resolution reconstruction method based on spectrum-space combination and gradient domain loss. The method includes steps: S1, obtaining a hyperspectral image; S2, dividing the hyperspectral image into a training set and a test set; S3, inputting the training set to a neural network with spectrum-space combination, and performing training by employing a combined loss of a spatial domain and a gradient domain; and S4, enabling the test set to pass through the neural network, and obtaining a final reconstruction result. According to the adoptedmethod, compared with the prior art, light weight is achieved in a network structure, the reconstruction quality is higher, and the anti-noise performance is great.

The invention belongs to the technical field of computed tomography image processing. According to the specific technical scheme, the CT image super-resolution reconstruction method based on the generative adversarial network comprises the following specific steps: 1, establishing a dense connection relationship among different residual blocks based on a multi-stage dense residual block generatornetwork; 2, adding a bottleneck layer to the front end of each dense residual block; 3, optimizing the global network by adopting the Wasserstein distance loss and the VGG feature matching loss; 4, arranging a multi-path generator based on the sequence from thick to thin; 5, generating an image based on conditional expression generative adversarial learning; 6, reconstructing a CT image super-resolution reconstruction framework of the generative adversarial network based on multiple paths of conditions from coarse to fine; 7, reconstructing a loss function. According to the method, network redundancy is reduced, feature multiplexing among different residual blocks is realized, the maximum information transmission of the network is realized, the feature utilization rate is improved, and thereconstructed image quality is greatly improved.

The invention discloses a self-adapting image compressive sampling method based on a multi-dimension saliency map, and is used for solving the problem of sampling resource waste due to the average allocation of the sampling rate to the image during compressive sampling. The method mainly comprises the steps of: carrying out support value transformation (SVT) on an sampled image and calculating toobtain a saliency map of the image; determining a vision salient region and a vision non-salient region according to the saliency map; allocating measurement data, allocating more sampling resources to the vision salient region; and reconstructing the measurement data obtained by self-adapting sampling through a nonlinear reconstructing algorithm to finally obtain an reconstructed image. Comparedwith the prior art, the method has the advantages that: when the compressive measurement of the image is carried out, according to the difference in people vision attention regions, self-adapting sampling resource allocation can be achieved based on different attention regions, thus the utilization rate of the sampling resources is increased and the quality of the recovered image is improved simultaneously. The method can be used for self-adapting compressive sampling of natural images, remote sensing images and the like, and has broad application prospects in low-cost imaging equipment.

The invention discloses a three-dimensional video encoding method based on depth image rendering. The encoding method disclosed by the invention comprises the following steps of: projecting an original left-viewpoint colorful image to a right viewpoint from a left viewpoint through a method based on the depth image rendering, so as to obtain a residual image of an rendered image of an original right-viewpoint colorful image and the original left-viewpoint colorful image; carrying out pre-processing operation on the residual image and encoding the original left-viewpoint colorful image and the pre-processed residual image; and finally, obtaining a finally-reconstructed image of the decoded right-viewpoint colorful image according to the rendered image of the decoded right-viewpoint colorful image and the decoded residual image. According to the invention, on the premise of ensuring the higher reconstruction quality of the right-viewpoint colorful image, the encoding efficiency of a three-dimensional video is greatly improved.

The invention discloses a three-dimensional model reconstruction method and device, equipment and a storage medium, and relates to the field of three-dimensional modeling. The method comprises the steps of obtaining a single target image, wherein the target image comprises an image of a target reconstruction object; performing feature extraction on the target image to obtain a target feature map corresponding to the target image; inputting the target feature map into a grid reconstruction network; the target feature map is input into a voxel reconstruction network, the grid reconstruction network and the voxel reconstruction network are in semantic connection, the grid reconstruction network is used for reconstructing a three-dimensional grid model of a target reconstruction object according to the target feature map, and the voxel reconstruction network is used for reconstructing a voxel model of the target reconstruction object according to the target feature map; and constructing atarget three-dimensional model according to the target grid information output by the grid reconstruction network. In the embodiment of the invention, the three-dimensional model reconstruction of thetarget reconstruction object can be realized by only needing a single image, the requirement on a two-dimensional image required by three-dimensional reconstruction is reduced, and the three-dimensional reconstruction process is simplified.

The invention discloses a compressed sensing coding and decoding method for a distributed video coding non-critical frame. The method can calculate the number of required measured values for compressed sensing of corresponding image blocks in the non-critical frame according to DCT coefficient characteristics for reconstituting image blocks in the non-critical frame. The method can divide the image blocks into different type according to the required measurement number and can choose different coding methods according to the types of the image blocks. The compressed sensing coding and decoding method reduces complexity of compressed sensing coding and decoding of the distributed video coding non-critical frame and improves the quality of reconstitution images of the non-critical frame.

The invention discloses a multivariate compressed sensing reconstruction method based on a wavelet HMT (Hidden Markov Tree) model. The multivariate compressive sensing reconstruction method comprises the following steps of: carrying out wavelet transformation on an image, preserving a low-frequency transform coefficient, and carrying out multivariate compressive sampling on a high-frequency transform coefficient to obtain a multivariate measurement vector Y; reconstructing an initial image by using the existing MPA (Multivariate Pursuit Algorithm); calculating the posterior state probability of the high-frequency transform coefficient of the reconstructed image in a large magnitude state; updating a weighted value of the high-frequency transform coefficient; reconstructing the image by using a WMPA algorithm; returning to the second step if the condition that an appointed repeated interation weighting reconstruction times I is equal to 2 is not obtained; or else, obtaining the reconstructed image of the original image. The multivariate compressive sensing reconstruction method based on the wavelet HMT model, disclosed by the invention, has a good reconstruction effect and is applicable to both medical images and natural images.

The invention discloses a compressed sensing based color imaging device and a compressed sensing based color imaging method. The compressed sensing based color imaging device comprises a PC (personal computer), a DLP (digital light projector), a target image, a single-pixel photon detector and a data collection and control module. The PC, the DLP and the single-pixel photon detector are connected with the data collection and control module. The PC generates a two-dimensional random binary projected intensity image. The data collection and control module controls the DLP to perform binary intensity image projection on the target image under red, green, blue and white structured light sequentially to acquire digital signals of the red, green, blue and white structured light respectively and send the digital signals to the PC, reconstructs the signals of the red, green, blue and white structured light through compressed sensing to acquire four monochrome gray level images, and performing color fusion on the four monochrome gray level images to acquire a final color image. The compressed sensing based color imaging device and the compressed sensing based color imaging method have the advantages that light filters are not needed, and simple structure and low cost are achieved; sampling frequency can be reduced, and color fidelity can be guaranteed without reduction of image resolution.

The invention relates to a distributed compressed sensing video encoding and decoding method based on foreground-background separation, comprising the following steps: dividing a video according to the frame sequence into key frames (I frames) and non-key frames (P frames) based on the requirements of reconstruction accuracy and real-time performance; for the I frames, selecting an appropriate observation matrix to measure the I frames; for the P frames, performing foreground-background separation on the P frames and the corresponding I frames and then using the same observation matrix to measure the separated foreground, completing foreground-background separation in a differential manner, introducing decoding mode judgment at an encoder, classifying the decoding modes of the P frame in a GOP into three categories: a skip mode, a normal mode and a dictionary learning mode, and using the same observation matrix to measure the difference. By using the method, the complexity of the encoder can be reduced.

The invention discloses a depth image coding method which is characterized in that a maximum tolerable distortion distribution image of an original left viewpoint depth image is obtained according to influence of depth distribution on virtual viewpoint image drawing and by combining the visual characteristics of human eyes, a drawing image of an original right viewpoint depth image is obtained in the method based on depth image drawing, the original left viewpoint depth image is coded according to the maximum tolerable distortion distribution image and the set coding forecast structure to obtain a decoded left viewpoint depth image, the original right viewpoint depth image and a residual image of the drawing image of the original right viewpoint depth image are coded according to the set coding forecast structure to obtain a decoded residual image, and finally a reconstructed image of a decoded right viewpoint depth image is obtained according to a drawing image of the decoded right viewpoint depth image and the decoded residual image. The depth image coding method has the advantage of greatly improving depth image coding efficiency on the basis of ensuring virtual viewpoint image drawing performance.

The invention provides an image compression encryption method and a cloud auxiliary decryption method based on compressed sensing and optical transformation. The encryption method comprises the following steps: generating a chaotic sequence by respectively utilizing an LTS system and a 2D-LASM system according to a hash key and an initial key generated by a plaintext image P; dividing the plaintext image P into an approximate component LL and three detail components HL, LH and HH by using discrete wavelet transform; performing lossless encryption on the approximate component LL by using a chaotic sequence X1, and performing lossy encryption on the detail components HL, LH and HH by using chaotic sequences U1 and K1; combining the encrypted matrixes LL1 and H1 into a complex domain matrix LLH1, and performing double random phase encoding on the complex domain matrix LLH1 by using two random phase mask matrixes to obtain matrixes LL2 and H2; quantifying the matrix LL2 and the matrix H2;carrying out the random pixel scrambling on the quantized matrix by using a chaotic sequence Z1; recombining the scrambled matrixes to generate a sequence B, and diffusing the sequence B to obtain a ciphertext image C.

The invention provides a compressive sampling method, which includes: acquiring signals of a target object, selecting a sparse base capable of indicating the signals of the target object sparsely; generating quick projection operators according to the initial sparse base, a preset random permutation matrix and a random matrix, solving the optimum value of subcomponents according to the quick projection operators, and obtaining optimized sparse components; determining the sparse coefficient of the optimized sparse components in the initial sparse base, and reconfiguring to-be-sampled signals according to the sparse coefficient. Data in different data quantity can be acquired on blocks different in sparse degree by the quick compressive sampling method, and the random projection operators have lower computing complexity so that higher reconfiguring quality can be obtained. Besides, the invention discloses a compressive sampling device.

The invention relates to a compressive sensing-based image decoding method. The compressive sensing-based image decoding method comprises the following steps of: performing compressive sensing (CS) reconstruction on an image signal acquired through inverse quantization at a decoding end, wherein the CS reconstruction performed on the image signal can be realized by solving the optimization problem of norm of the following formula; and converting the solved column vector into a matrix to decode the image. In order to improve the quality of the CS reconstruction, the method also comprises the step of performing block combination on the image, namely combining p*p image blocks into one image block, before performing the CS reconstruction on the image signal, wherein the line number / column number of the combined image block is p times that of the image blocks before the combination. In the method, the value of a TV operator on the block edge is further improved; when i is equal to n in the image block matrix In*n, a horizontal operator is defined as Ii-1, j-Iij; and when j is equal to n, a vertical operator is defined as Ii, j-1-Iij. All the improvement of the method is concentrated at the decoding end and an encoding end does not need any alteration, so a better effect can be achieved compared with the conventional image compression standard.

The invention discloses a video coding and decoding method based on multiple description CS measurement value. The coding method comprises steps: a video image sequence is divided into a key frame and a CS frame; block-based measurement, quantization and entropy coding are adopted for the key frame; a multiple description idea is adopted for the CS frame to describe a block-based measurement value in two ways; and quantization and entropy coding are separately carried out. The decoding method comprises steps: after key frame data are received, inverse quantization is firstly carried out on entropy decoding to obtain a measurement value, the key frame block is initially reconstructed, and multiple hypothesis prediction and reconstruction are carried out inside the frame; after CS frame multiple description code stream is received, inverse quantization is carried out on entropy decoding; a measurement value based on multiple description is generated; and CS frame multiple description adaptive inter-frame multiple hypothesis reconstruction is carried out. In the condition of not increasing complexity of the coding end, the reconstruction quality and robustness of a video image are improved.

The invention discloses a compressed sensing image reconstruction method based on relevance vector grouping, which mainly solves the problems of inaccuracy and low robustness of compressed sensing image reconstruction. The realization process is as follows: 1) receiving an observation matrix and an observation vector; 2) obtaining an initial relevance vector by the observation vector and a sending matrix; 3) dividing the relevance vector into sub-relevance vectors according to the spatial neighbourhood relationship of wavelet coefficients; 4) adding a component in each sub-relevance vector and sequencing the components; 5) updating the reconstructed wavelet high-frequency coefficients and observation vectors on the basis of a Bayesian framework according to the sequencing order; 6) carrying out invert wavelet transform on the reserved low-frequency wavelet decomposition coefficients and the reconstructed high-frequency wavelet coefficients to obtain a reconstructed image. Compared with OMP and BEPA methods, the compressed sensing image reconstruction method based on relevance vector grouping disclosed by the invention has the advantages of high quality and good robustness of the reconstructed image, and can be used for reconstruction for natural images and medical images.

The invention provides a highly efficient technique for encoding a multi-channel audio signal. The invention relies on the basic principle of encoding a first signal representation of one or more of the multiple channels in a first encoder (130) and encoding a second signal representation of one or more of the multiple channels in a second, multi-stage, encoder (140). This procedure is significantly enhanced by providing a controller (150) for adaptively allocating a number of encoding bits among the different encoding stages of the second, multi-stage, encoder (140) in dependence on multi-channel audio signal characteristics.

The invention discloses a time-space video compressed sensing method based on a convolutional network, and mainly aims at solving the problems that in the prior art, the video compression time-space balance and the video reconstruction real-time performance are poor. The scheme of the method comprises the steps that a training data set is prepared; a network structure of a time-space video compressed sensing method is designed; training and testing files are written according to the designed network structure; a network of the time-space video compressed sensing method is trained; and the network of the time-space video compressed sensing method is tested. The network of the time-space video compressed sensing method adopts an observation technology of simultaneous time-space compression are conducted simultaneously and a reconstruction technology of using 'time-space blocks' to enhance the time-space correlation, not only can real-time video reconstruction be achieved, but also the reconstruction result has the high time-space balance, the reconstruction quality is high and stable, and the network can be used for compressed transmission of a video and follow-up video reconstruction.

The invention provides a spectrum camera based on all-pass single-template complementary sampling and an imaging method for solving a technical problem that a conventional spectral imaging system is complex in structure and poor in spectral image reconstruction quality. The spectrum camera comprises a lens group, an encoding module, an observation module, and an image reconstruction processing module. The lens group is composed of a group of lenses. The encoding module uses an area array structure formed by random combination of multiple transmission array elements and reflection array elements, and forms an acute angle with the transmission direction of the spectral image, and is used for randomly encoding the transmission information and the reflection information of the spectral image. The observation module comprises a transmission observation module and a reflection observation module and is used for performing transmission observation and reflection observation on the encoded spectral image. The image reconstruction processing module simultaneously fuses two observation results to reconstruct the spectral image. The spectrum camera is simple in system structure, high in luminous flux, and short in exposure time, and can be used in the fields of mineral exploration, natural disaster monitoring, and military reconnaissance.

The invention discloses an image compressive sensing reconstruction system and method utilizing weighted structural group sparse regulations, and relates to the image recovery technology field. In the system, an initialization module, a routing selection module, a regularization mean square error minimum module and an image filtering processing module interact in sequence, and the image filtering processing module and the routing selection module interact. The image filtering processing module comprises an image overlap partitioning unit, an image similar block generation unit, a transformation domain weighted soft threshold filtering unit and an image block pixel domain averaging unit which interact in order. In the first stage, the image compressive sensing reconstruction method is adopted to obtain a reconstructed initial evaluation value of a compressive sensing image. In the second stage, on the basis of non-local similarity of the image, the reconstruction quality of the compressive sensing image is improved through multiple times of iteration by adopting optimization of weighted structural group sparse expressed regularization. Image textures and recovery effects of image edges can be improved, and the reconstruction quality of the compressive sensing image can be effectively improved.

The invention aims at providing a three-dimensional electrical impedance tomography method based on crisscross-arranged electrodes, comprising the following steps of: (1) putting the electrodes on the surface of a body to be measured of a three-dimensional image, wherein the electrodes are crisscross arranged on the surface of the body to be measured of the three-dimensional image; (2) obtaining a corresponding finite element model through the body to be measured of the three-dimensional image, carrying out data acquisition on the finite element model by the electrodes, and computing a differential voltage signal yi=vi-v0 according to the acquired data; (3) computing the approximate value of the change of the specific conductance of the body to be measured of the three-dimensional image; and (4) computing the obtained approximate value within the finite element model to be displayed, wherein the displayed image is a real-time difference image of the body to be measured of the three-dimensional image. The electrodes are crisscross arranged on the surface of the body to be measured of the three-dimensional image, compared with the prior art, the method can obviously improve the reconfiguration quality of the three-dimensional image and the detection sensitivity of a target.

The invention discloses a method for image processing based on a matrix variable variation self-encoder so that a problem of damaging the spatial structure by image vectorization processing can be solved and thus image reconstruction, denoising and completion can be realized. Different from the traditional VAE, the method has the following advantages: the input, hidden layer features, latent variable distribution parameters of the model are described by using an inherent expression form -2D matrix of the image; with the definition of matrix Gaussian distribution and related properties, an explicit expression of an objective function of a novel model is deduced and then model parameters are calculated by using a stochastic gradient descent algorithm. In the model, modeling of the spatial structure and statistical information of the image data is carried out well because the involved modeling process is oriented at the matrix variable, so that the image reconstruction quality is enhanced, noises are removed, and the image completion is realized.

The invention provides an adaptive partition compression and perception-based video compression method. The method comprises two steps, namely the step of adaptively partitioning video images, and classifying and assigning sampling rates for various image blocks. During the step of adaptively partitioning video images, a gray difference value between the adjacent pixels of a reference frame image is adopted as a basis for block size segmentation, and a partitioning threshold value T is set. The gray average difference value between the adjacent pixels of a current region block is compared with the threshold value, and the adaptive blocks of the video image are partitioned based on the quad-tree algorithm. In this way, flat regions are effectively separated from detail regions and edge regions. On the basis of the adaptive partitioning operation, an inter-frame difference value for the DCT coefficients of video pixels is adopted as a basis for partitioning, and various image blocks diversified in size are divided into three types, namely quickly changing blocks, transition blocks and slowly varying blocks. Meanwhile, appropriate sampling rates are assigned to different types of image blocks. The method is good in video reconstruction quality and short in reconstruction time. Under the same condition, the video reconstruction quality and the reconstruction time of the above method are better than those of the video uniform partitioning, compression and perception processing method.

The invention relates to a streaming media video compression method based on inter-frame correlation. The method comprises: step one, video compressive sensing coding based on bidirectional predictionis carried out; to be specific, front-rear inter-frame correlation and intra-frame correlation are introduced in a video coding end and are used as sampling rates of non-reference-frame block image reasonable allocation; measurement vectors of corresponding image blocks of the reference frame and the non-reference frame are obtained by using a same projection matrix and the image blocks are classified into three classes based energy difference values: a basic invariant block class, a slowly changing block class and a fast changing block class; a preliminary classification result is correctedby using the intra-frame correlation; and then a final classification result is obtained, different sampling rates are allocated to different classes of image blocks to carry out sampling, and the measurement vector point number of the current image block is adaptively adjusted to obtain a measurement vector. Therefore, the classification accuracy is improved.

The invention discloses a three-dimensional wavelet sub-band sorting and code stream packet sealing method in the telescopic video code, wherein the wavelet sub-band in the time domain is sorted according to the requirement of the sequential decode, and the wavelet sub-band in the time domain is divided into different levels; the wavelet sub-band in the time domain of the same level is sorted according to the size of the transmission distortion MES value; then the code stream after being sorted is packed, then transmitted to a receiving end, when the receiving end does not reach the time limit of the decode, then performs the retransmission when finding the package missing, with the retransmitting time smaller than or equal to the largest retransmitting time; when the receiving end reaches the time limit of the decode, the package missing is not retransmitted. The method provides an effective code rate transmission control method, thereby providing correct code stream distribution for the video code and the transmission, providing the code stream organizing and transmitting way of high performance three-dimensional retractability self-adapting to the isomery, the network bandwidth wave property and the time delay change and the terminal diversity of the user receiving.

The invention discloses a nonconvex compressed sensing image reconstruction method based on local similarity and local selection. The method comprises the following steps: 1) carrying out observation and reception after an image is partitioned; 2)utilizing a local growth method to carry out clustering on observation vectors of all the image blocks; 3) carrying out population initialization on the image block corresponding to each kind of observation vector according to the scheme that the polyatom direction and monatom direction coexist; 4) utilizing an improved genetic algorithm to carry out crossing, variation and selection operation based on a local selection mechanism on the populations obtained in the step 3), reconstituting corresponding image blocks and obtaining optimal atom combinations; 5) utilizing a clone selection optimization algorithm to study the optimal atom combinations on the aspects of dimension and displacement; and 6) piecing the image blocks obtained in the step 5) together in sequence to obtain a complete reconstructed image, and outputting the complete reconstructed image. The reconstructed image is good in visual effect and high in peak signal to noise ratio, and can be used for nonconvex compressed sensing reconstruction of image signals under the condition of low sampling rate.

The invention discloses a coding aperture spectral imaging method based on self-adaptive dictionary learning, and solves the problem that the reconstruction quality of a spectral image is poor due tothe fact that a redundant dictionary constructed by a traditional method cannot effectively and sparsely represent the target image in the previous coding aperture spectral imaging process. The self-adaptive learning is performed according to the measured value to obtain the redundant dictionary, and the method is used for improving the quality of the reconstructed spectral image. The method comprises the following steps: firstly, transforming an original coded aperture spectral imaging framework, and adopting an overlapping block measurement mode; estimating a plurality of spectral image blocks by using a least square method, constructing a training sample set, and performing adaptive training learning by using the sample set to obtain a new redundant dictionary; substituting the new dictionary into an imaging framework to reconstruct a target spectral image; and finally, carrying out loop iteration on the process until an optimal solution is obtained. The constructed redundant dictionary can adapt to a target image, and the spectral image reconstruction quality in coded aperture spectral imaging is greatly improved.

The invention provides a remote sensing image super resolution method based on dictionary learning, comprising the following steps: S1, performing dictionary learning of corresponding feature types based on an image library of different feature types; S2, identifying the feature type of an original image; and S3, carrying out an image super resolution reconstruction process using a corresponding feature type dictionary. The invention puts forward a super resolution method based on dictionary learning using the idea of feature classification. As the number of atoms in a dictionary used in the reconstruction process is reduced greatly, the dictionary scale is reduced greatly. The proportion of effective atoms in the classification dictionary used is increased significantly, so that the number of effective atoms in the dictionary used in the reconstruction process is almost the same as a general dictionary, and the speed of reconstruction is improved significantly while the quality of reconstruction is ensured. The process only involves the adjustment of parameters, the computation efficiency is high, and the reconstruction quality of remote sensing images is high.

The invention discloses a distributed video compressive sensing sampling method based on time correlation. According to the method, on the basis of establishing a non-feedback distributed video compressive sensing model, the time correlation among video frames is utilized fully; a sampling rate of each block is preliminarily allocated according to a size of the information of a video group of pictures occupied by the information of each picture block; mode judgment is carried out according inter-frame residuals; and a practical sampling rate of each small block is calculated, so self-adaptive sampling allocation is realized. According to the method, the reconstruction quality of a video is improved under the same total sampling rate, the energy saving amount resulting from reducing the sampling times far exceeds the extra energy consumption amount resulting from employing a dynamic sampling rate algorithm, no feedback channel is employed, and the time delay is relatively low. According to the method, the problem that the time correlation is not taken into full consideration in an existing distributed video compressive sensing sampling process is solved, and the method has good practical value.

The invention discloses an adaptive rendering method using linear prediction, comprising the following steps: S1, defining prediction windows, and constructing multiple linear models through a recursive least-squares iteration process according to different sizes of the prediction windows; S2, calculating error caused by linear model prediction through recursive error analysis, and choosing the best prediction size for the prediction windows; and S3, distributing more light samples by use of an adaptive sampling method and according to the calculated prediction error to complete rendering. According to the adaptive rendering method using linear prediction provided by the invention, multiple pixels are reconstructed by independent linear models at the same time, costly error calculation is needed only at a small number of pixels, and therefore, the computational overhead is reduced greatly.