ADCTVQ: A New Method for Image Coding and Compressing

This paper presents a new method for image coding and compressing-ADCTVQ(Adptive Discrete Cosine Transform Vector Quantization). In this method, DCT conforms to visual properties and has an encoding ability which is inferior only to the best transform KLT. Its vector quantization can maintain the minimum quantization distortions and greatly increase the compression ratio. In order to improve compression efficiency, an adaptive strategy of selecting reserved region patterns is applied to preserving the high energy at the same compression ratio. The experiment results show that they are satisfactory at the compression ration ratio if greater than 20.

图象ADCT混合熵编码方法

图象ＡＤＣＴ混合熵编码方法衡伟，富煜清，程时昕（东南大学无线电系，南京２１００１８）ＡＤＣＴ熵编码方法以其优异的性能，成为１９８８年ＪＰＥＧ建议的基本方法，它与预测法结合的ＡＤＣＴ熵编码方法被１９９０年通过的ＣＣＴＴＴＨ．２６１标准所采纳［１，２］．...

DCT专用芯片A121及其在ADCT图象编译码器中的应用

介绍了离散余弦变换（DCT）的原理及其专用集成电路芯片A121的结构与特性，对实现自适应离散余弦变换（ADCT）图象编码的具体算法作了讨论。给出A121在ADCT图象编译码器中实际应用的例子。该编译码器以A121为核心，采用8098作控制器，灵活地实现图象数据编、解码的ADCT运算。

Color Image Compression and Encryption Algorithm Based on 2D Compressed Sensing and Hyperchaotic System

With the advent of the information security era,it is necessary to guarantee the privacy,accuracy,and dependable transfer of pictures.This study presents a new approach to the encryption and compression of color images.It is predicated on 2D compressed sensing(CS)and the hyperchaotic system.First,an optimized Arnold scrambling algorithm is applied to the initial color images to ensure strong security.Then,the processed images are con-currently encrypted and compressed using 2D CS.Among them,chaotic sequences replace traditional random measurement matrices to increase the system’s security.Third,the processed images are re-encrypted using a combination of permutation and diffusion algorithms.In addition,the 2D projected gradient with an embedding decryption(2DPG-ED)algorithm is used to reconstruct images.Compared with the traditional reconstruction algorithm,the 2DPG-ED algorithm can improve security and reduce computational complexity.Furthermore,it has better robustness.The experimental outcome and the performance analysis indicate that this algorithm can withstand malicious attacks and prove the method is effective.

Target acquisition performance in the presence of JPEG image compression

This paper presents an investigation on the effect of JPEG compression on the similarity between the target image and the background,where the similarity is further used to determine the degree of clutter in the image.Four new clutter metrics based on image quality assessment are introduced,among which the Haar wavelet-based perceptual similarity index,known as HaarPSI,provides the best target acquisition prediction results.It is shown that the similarity between the target and the background at the boundary between visually lossless and visually lossy compression does not change significantly compared to the case when an uncompressed image is used.In future work,through subjective tests,it is necessary to check whether this presence of compression at the threshold of just noticeable differences will affect the human target acquisition performance.Similarity values are compared with the results of subjective tests of the well-known target Search_2 database,where the degree of agreement between objective and subjective scores,measured through linear correlation,reached a value of 90%.

Multispectral image compression and encryption method based on tensor decomposition in wavelet domain

Multispectral image compression and encryption algorithms commonly suffer from issues such as low compression efficiency,lack of synchronization between the compression and encryption proces-ses,and degradation of intrinsic image structure.A novel approach is proposed to address these is-sues.Firstly,a chaotic sequence is generated using the Lorenz three-dimensional chaotic mapping to initiate the encryption process,which is XORed with each spectral band of the multispectral image to complete the initial encryption of the image.Then,a two-dimensional lifting 9/7 wavelet transform is applied to the processed image.Next,a key-sensitive Arnold scrambling technique is employed on the resulting low-frequency image.It effectively eliminates spatial redundancy in the multispectral image while enhancing the encryption process.To optimize the compression and encryption processes further,fast Tucker decomposition is applied to the wavelet sub-band tensor.It effectively removes both spectral redundancy and residual spatial redundancy in the multispectral image.Finally,the core tensor and pattern matrix obtained from the decomposition are subjected to entropy encoding,and real-time chaotic encryption is implemented during the encoding process,effectively integrating compression and encryption.The results show that the proposed algorithm is suitable for occasions with high requirements for compression and encryption,and it provides valuable insights for the de-velopment of compression and encryption in multispectral field.

Rice Crop Digital Image Compression Technology Based on Discrete Fourier Transform

[Objective] To study the digital image compression technology in rice monitoring system. [Method] A digital image compression technology program based on the discrete Fourier transform was proposed, and simulation experiments were carried out to compress the image at different compression ratios. [Result] When com- pression ratios were less than 30, the compression ratio, image entropy, average codeword length, coding efficiency and redundancy which reflected the quality of the coding, and the parameter PSNR which estimated the fidelity of the compressed im- age were all achieved good results that human eye could barely percept the differ- ence between the original image and decompressed image; and when the compres- sion ratios were more than 30, there was a certain distortion in the decompressed image. And when the compression ratio was 91.516 3, although the image had some distortion, the PSNR was still achieved to 21.528 2, and human eye could accept the decompressed image intuitively within the acceptable error range. [Conclusion] The results show that the proposed image compression program is a viable, effective, and better image compression technology which can satisfy the requirements of the crop monitoring system on image storage, transforming and transporting.

Application Research of Image Compression Technology in Monitoring System of Rape Crop in Areas of Qinling Mountains

[Objective] The aim was to present a proposal about a new image compression technology, in order to make the image be able to be stored in a smaller space and be transmitted with smaller bit rate on the premise of guaranteeing image quality in the rape crop monitoring system in Qinling Mountains. [Method] In the proposal, the color image was divided into brightness images with three fundamental colors, followed by sub-image division and DCT treatment. Then, coefficients of transform domain were quantized, and encoded and compressed as per Huffman coding. Finally, decompression was conducted through inverse process and decompressed images were matched. [Result] The simulation results show that when compression ratio of the color image of rape crops was 11.972 3∶1, human can not distinguish the differences between the decompressed images and the source images with naked eyes; when ratio was as high as 53.565 6∶1, PSNR was still above 30 dD,encoding efficiency achieved over 0.78 and redundancy was less than 0.22. [Conclusion] The results indicate that the proposed color image compression technology can achieve higher compression ratio on the premise of good image quality. In addition, image encoding quality and decompressed images achieved better results, which fully met requirement of image storage and transmission in monitoring system of rape crop in the Qinling Mountains.

Characterization of neurovascular compression in facial neuralgia patients by 3D high-resolution MRI and image fusion technique

Objective:To describe the anatomical characteristics and patterns of neurovascular compression (NVC) in patients suffering trigeminal neuralgia(TN) by 3D high-resolution magnetic resonance imaging(MRI) method and image fusion technique.Methods:The anatomic structure of trigeminal nerve,brain stem and blood vessel was observed in 100 consecutive TN patients by 3D high resolution MRI(3D SPGR,contrast-enhanced T1 3D MP-RAGE and T2/T1 3D FIESTA). The 3D image sources were fused and visualized using 3D DOCTOR software.Results:One or several NVC sites,which usually appeared 0-9.8 mm away from brain stem,were found on the symptomatic side in 93%of the TN cases.Superior cerebellar artery was involved in 76%(71/93) of these cases.The other vessels including antero-inferior cerebellar artery,vertebral artery, basilar artery and veins also contributed to the occurrence of NVC.The NVC sites were found to be located in the proximal segment in 42%of these cases(39/93) and in the distal segment in 45% (42/93).Nerve dislocation or distortion was observed in 32%(30/93).Conclusions:Various 3D high resolution MRI methods combined with the image fusion technique could provide pathologic anatomic information for the diagnosis and treatment of TN.

Specimen aspect ratio and progressive field strain development of sandstone under uniaxial compression by three-dimensional digital image correlation

The complete stress-strain characteristics of sandstone specimens were investigated in a series of quasistatic monotonic uniaxial compression tests.Strain patterns development during pre-and post-peak behaviours in specimens with different aspect ratios was also examined.Peak stress,post-peak portion of stress-strain,brittleness,characteristics of progressive localisation and field strain patterns development were affected at different extents by specimen aspect ratio.Strain patterns of the rocks were obtained by applying three-dimensional(3D) digital image correlation(DIC) technique.Unlike conventional strain measurement using strain gauges attached to specimen,3D DIC allowed not only measuring large strains,but more importantly,mapping the development of field strain throughout the compression test,i.e.in pre-and post-peak regimes.Field strain development in the surface of rock specimen suggests that strain starts localising progressively and develops at a lower rate in pre-peak regime.However,in post-peak regime,strains increase at different rates as local deformations take place at different extents in the vicinity and outside the localised zone.The extent of localised strains together with the rate of strain localisation is associated with the increase in rate of strength degradation.Strain localisation and local inelastic unloading outside the localised zone both feature post-peak regime.

A hybrid quantum encoding algorithm of vector quantization for image compression

Many classical encoding algorithms of vector quantization （VQ） of image compression that can obtain global optimal solution have computational complexity O（N）. A pure quantum VQ encoding algorithm with probability of success near 100% has been proposed, that performs operations 45√N times approximately. In this paper, a hybrid quantum VQ encoding algorithm between the classical method and the quantum algorithm is presented. The number of its operations is less than √N for most images, and it is more efficient than the pure quantum algorithm.

A Novel Fractal Wavelet Image Compression Approach

By investigating the limitation of existing wavelet tree based image compression methods, we propose a novel wavelet fractal image compression method in this paper. Briefly, the initial errors are appointed given the different levels of importance accorded the frequency sublevel band wavelet coefficients. Higher frequency sublevel bands would lead to larger initial errors. As a result, the sizes of sublevel blocks and super blocks would be changed according to the initial errors. The matching sizes between sublevel blocks and super blocks would be changed according to the permitted errors and compression rates. Systematic analyses are performed and the experimental results demonstrate that the proposed method provides a satisfactory performance with a clearly increasing rate of compression and speed of encoding without reducing SNR and the quality of decoded images. Simulation results show that our method is superior to the traditional wavelet tree based methods of fractal image compression.

Empirical data decomposition and its applications in image compression

A nonlinear data analysis algorithm, namely empirical data decomposition （EDD） is proposed, which can perform adaptive analysis of observed data. Analysis filter, which is not a linear constant coefficient filter, is automatically determined by observed data, and is able to implement multi-resolution analysis as wavelet transform. The algorithm is suitable for analyzing non-stationary data and can effectively wipe off the relevance of observed data. Then through discussing the applications of EDD in image compression, the paper presents a 2-dimension data decomposition framework and makes some modifications of contexts used by Embedded Block Coding with Optimized Truncation （EBCOT） . Simulation results show that EDD is more suitable for non-stationary image data compression.

Strain field investigation of limestone specimen under uniaxial compression loads using particle image velocimetry

The particle image velocimetry (PIV) method was used to investigate the full-field displacements and strains of the limestone specimen under external loads from the video images captured during the laboratory tests.The original colorful video images and experimental data were obtained from the uniaxial compression test of a limestone.To eliminate perspective errors and lens distortion,the camera was placed normal to the rock specimen exposure.After converted into a readable format of frame images,these videos were transformed into the responding grayscale images,and the frame images were then extracted.The full-field displacement field was obtained by using the PIV technique,and interpolated in the sub-pixel locations.The displacement was measured in the plane of the image and inferred from two consecutive images.The local displacement vectors were calculated for small sub-windows of the images by means of cross-correlation.The video images were interrogated in a multi-pass way,starting off with 64×64 images,ending with 16×16 images after 6 iterations,and using 75% overlap of the sub-windows.In order to remove spurious vectors,the displacements were filtered using four filters:signal-to-noise ratio filter,peak height filter,global filter and local filter.The cubic interpolation was utilized if the displacements without a number were encountered.The full-field strain was then obtained using the local least square method from the discrete displacements.The strain change with time at different locations was also investigated.It is found that the normal strains are dependant on the locations and the crack distributions.Between 1.0 and 5.0 s prior to the specimen failure,normal strains increase rapidly at many locations,while a stable status appears at some locations.When the specimen is in a failure status,a large rotation occurs and it increases in the inverse direction.The strain concentration bands do not completely develop into the large cracks,and meso-cracks are not visible in some bands.The techniques presented here may improve the traditional measurement of the strain field,and may provide a lot of valuable information in investigating the deformation/failure mechanism of rock materials.

An effective fractal image compression algorithm based on plane fitting

A new method using plane fitting to decide whether a domain block is similar enough to a given range block is proposed in this paper. First, three coefficients are computed for describing each range and domain block. Then, the best-matched one for every range block is obtained by analysing the relation between their coefficients. Experimental results show that the proposed method can shorten encoding time markedly, while the retrieved image quality is still acceptable. In the decoding step, a kind of simple line fitting on block boundaries is used to reduce blocking effects. At the same time, the proposed method can also achieve a high compression ratio.

An improved fast fractal image compression using spatial texture correlation

This paper utilizes a spatial texture correlation and the intelligent classification algorithm （ICA） search strategy to speed up the encoding process and improve the bit rate for fractal image compression. Texture features is one of the most important properties for the representation of an image. Entropy and maximum entry from co-occurrence matrices are used for representing texture features in an image. For a range block, concerned domain blocks of neighbouring range blocks with similar texture features can be searched. In addition, domain blocks with similar texture features are searched in the ICA search process. Experiments show that in comparison with some typical methods, the proposed algorithm significantly speeds up the encoding process and achieves a higher compression ratio, with a slight diminution in the quality of the reconstructed image; in comparison with a spatial correlation scheme, the proposed scheme spends much less encoding time while the compression ratio and the quality of the reconstructed image are almost the same.

Nonlinear Adaptive Wavelet Transform for Lossless Image Compression

The paper presents a class of nonlinear adaptive wavelet transforms for lossless image compression. In update step of the lifting the different operators are chosen by the local gradient of original image. A nonlinear morphological predictor follows the update adaptive lifting to result in fewer large wavelet coefficients near edges for reducing coding. The nonlinear adaptive wavelet transforms can also allow perfect reconstruction without any overhead cost. Experiment results are given to show lower entropy of the adaptive transformed images than those of the non-adaptive case and great applicable potentiality in lossless image compresslon.

Detecting JPEG image forgery based on double compression

Detecting the forgery parts from a double compressed image is very important and urgent work for blind authentication. A very simple and efficient method for accomplishing the task is proposed. Firstly, the probabilistic model with periodic effects in double quantization is analyzed, and the probability of quantized DCT coefficients in each block is calculated over the entire iraage. Secondly, the posteriori probability of each block is computed according to Bayesian theory and the results mentioned in first part. Then the mean and variance of the posteriori probability are to be used for judging whether the target block is tampered. Finally, the mathematical morphology operations are performed to reduce the false alarm probability. Experimental results show that the method can exactly locate the doctored part, and through the experiment it is also found that for detecting the tampered regions, the higher the second compression quality is, the more exact the detection efficiency is.

An Improved EZW Hyperspectral Image Compression

The paper describes an efficient lossy and lossless three dimensional (3D) image compression of hyperspectral images. The method adopts the 3D spatial-spectral hybrid transform and the proposed transform-based coder. The hybrid transforms are that Karhunen-Loève Transform (KLT) which decorrelates spectral data of a hyperspectral image, and the integer Discrete Wavelet Transform (DWT) which is applied to the spatial data and produces decorrelated wavelet coefficients. Our simpler transform-based coder is inspired by Shapiro’s EZW algorithm, but encodes residual values and only implements dominant pass incorporating six symbols. The proposed method will be examined on AVIRIS images and evaluated using compression ratio for both lossless and lossy compression, and signal to noise ratio (SNR) for lossy compression. Experimental results show that the proposed image compression not only is more efficient but also has better compression ratio.

Static Image Compression Based on Fractal Theory

In this paper, image compression and decompression are realized on a personal computer based on fractal theory. The algorithm is effectiveas as the reconstructed image is similar to the original. In the algorithm, the formulas for contrast scaling and luminance shift are simplified,and the Hausdorff distance is replaced by the Euclidean distance. Thus, the calculation load is reduced. The formula for compression ratio is presented for an ideal situation, from which one can analyze how the different factors influence image compression ratio.