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.

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.

Chaos-based encryption for fractal image coding

A chaos-based cryptosystem for fractal image coding is proposed. The Renyi chaotic map is employed to determine the order of processing the range blocks and to generate the keystream for masking the encoded sequence. Compared with the standard approach of fraetal image coding followed by the Advanced Encryption Standard, our scheme offers a higher sensitivity to both plaintext and ciphertext at a comparable operating efficiency. The keystream generated by the Renyi chaotic map passes the randomness tests set by the United States National Institute of Standards and Technology, and so the proposed scheme is sensitive to the key.

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.

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.

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.

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.

Medical Image Compression Based on Wavelets with Particle Swarm Optimization

This paper presents a novel method utilizing wavelets with particle swarm optimization(PSO)for medical image compression.Our method utilizes PSO to overcome the wavelets discontinuity which occurs when compressing images using thresholding.It transfers images into subband details and approximations using a modified Haar wavelet(MHW),and then applies a threshold.PSO is applied for selecting a particle assigned to the threshold values for the subbands.Nine positions assigned to particles values are used to represent population.Every particle updates its position depending on the global best position(gbest)(for all details subband)and local best position(pbest)(for a subband).The fitness value is developed to terminate PSO when the difference between two local best(pbest)successors is smaller than a prescribe value.The experiments are applied on five different medical image types,i.e.,MRI,CT,and X-ray.Results show that the proposed algorithm can be more preferably to compress medical images than other existing wavelets techniques from peak signal to noise ratio(PSNR)and compression ratio(CR)points of views.

Low complexity DCT-based distributed source coding with Gray code for hyperspectral images

To compress hyperspectral images, a low complexity discrete cosine transform （DCT）-based distributed source coding （DSC） scheme with Gray code is proposed. Unlike most of the existing DSC schemes, which utilize transform in spatial domain, the proposed algorithm applies transform in spectral domain. Set-partitioning-based approach is applied to reorganize DCT coefficients into waveletlike tree structure and extract the sign, refinement, and significance bitplanes. The extracted refinement bits are Gray encoded. Because of the dependency along the line dimension of hyperspectral images, low density paritycheck-（LDPC）-based Slepian-Wolf coder is adopted to implement the DSC strategy. Experimental results on airborne visible/infrared imaging spectrometer （AVIRIS） dataset show that the proposed paradigm achieves up to 6 dB improvement over DSC-based coders which apply transform in spatial domain, with significantly reduced computational complexity and memory storage.

Edge Oriented Compression Coding on Image Sequence

An edge oriented image sequence coding scheme is presented. On the basis of edge detecting, an image could be divided into the sensitized region and the smooth region. In this scheme, the architecture of sensitized region is approximated with linear type of segments. Then a rectangle belt is constructed for each segment. Finally, the gray value distribution in the region is fitted by normal forms polynomials. The model matching and motion analysis are also based on the architecture of sensitized region. For the smooth region we use the run length scanning and linear approximating. By means of normal forms polynomial fitting and motion prediction by matching, the images are compressed. It is shown through the simulations that the subjective quality of reconstructed picture is excellent at 0.0075 bit per pel.

Improving Reconstructed Image Quality via Hybrid Compression Techniques

Data compression is one of the core fields of study for applications of image and video processing.The raw data to be transmitted consumes large bandwidth and requires huge storage space as a result,it is desirable to represent the information in the data with considerably fewer bits by the mean of data compression techniques,the data must be reconstituted very similarly to the initial form.In this paper,a hybrid compression based on Discrete Cosine Transform(DCT),DiscreteWavelet Transform(DWT)is used to enhance the quality of the reconstructed image.These techniques are followed by entropy encoding such as Huffman coding to give additional compression.Huffman coding is optimal prefix code because of its implementation is more simple,faster,and easier than other codes.It needs less execution time and it is the shortest average length and the measurements for analysis are based upon Compression Ratio,Mean Square Error(MSE),and Peak Signal to Noise Ratio(PSNR).We applied a hybrid algorithm on(DWT–DCT 2×2,4×4,8×8,16×16,32×32)blocks.Finally,we show that by using a hybrid(DWT–DCT)compression technique,the PSNR is reconstructed for the image by using the proposed hybrid algorithm(DWT–DCT 8×8 block)is quite high than DCT.

A New Quality Improving Scheme for VQ Decompressed Images Based on DWT

The better compression rate can be achieved by the traditional vector quantization （VQ） method, and the quality of the recovered image can also be accepted. But the decompressed image quality can not be promoted efficiently, so how to balance the image compression rate and image recovering quality is an important issue, in this paper, an image is transformed by discrete wavelet transform （DWT） to generate its DWT transformed image which can be compressed by the VQ method further. Besides, we compute the values between the DWT transformed image and decompressed DWT transformed image as the difference matrix which is the adjustable basis of the decompressed image quality. By controlling the deviation of the difference matrix, there can be nearly Iossless compression for the VQ method. Experimental results show that when the number of compressed bits by our method is equal to the number of those bits compressed by the VQ method, the quality of our recovered image is better. Moreover, the proposed method has more compression capability comparing with the VQ scheme.

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.

A New Encryption-Then-Compression Scheme on Gray Images Using the Markov Random Field

Compressing encrypted images remains a challenge.As illustrated in our previous work on compression of encrypted binary images,it is preferable to exploit statistical characteristics at the receiver.Through this line,we characterize statistical correlations between adjacent bitplanes of a gray image with the Markov random field(MRF),represent it with a factor graph,and integrate the constructed MRF factor graph in that for binary image reconstruction,which gives rise to a joint factor graph for gray images reconstruction(JFGIR).By exploiting the JFGIR at the receiver to facilitate the reconstruction of the original bitplanes and deriving theoretically the sum-product algorithm(SPA)adapted to the JFGIR,a novel MRF-based encryption-then-compression(ETC)scheme is thus proposed.After preferable universal parameters of the MRF between adjacent bitplanes are sought via a numerical manner,extensive experimental simulations are then carried out to show that the proposed scheme successfully compresses the first 3 and 4 most significant bitplanes(MSBs)for most test gray images and the others with a large portion of smooth area,respectively.Thus,the proposed scheme achieves significant improvement against the state-of-the-art leveraging the 2-D Markov source model at the receiver and is comparable or somewhat inferior to that using the resolution-progressive strategy in recovery.

Fractal image encoding based on adaptive search

Finding the optimal algorithm between an efficient encoding process and therate distortion is the main research in fractal image compression theory. A new method has beenproposed based on the optimization of the Least-Square Error and the orthogonal projection. A largenumber of domain blocks can be eliminated in order to speed-up fractal image compression. Moreover,since the rate-distortion performance of most fractal image coders is not satisfactory, an efficientbit allocation algorithm to improve the rate distortion is also proposed. The implementation andcomparison have been done with the feature extraction method to prove the efficiency of the proposedmethod.

Second Generation Wavelet Applied to Lossless Compression Coding of Image

In this paper, the second generation wavelet transform is applied to image lossless coding, according to its characteristic of reversible integer wavelet transform. The second generation wavelet transform can provide higher compression ratio than Huffman coding while it reconstructs image without loss compared with the first generation wavelet transform. The experimental results show that the se cond generation wavelet transform can obtain excellent performance in medical image compression coding.

Simultaneous Speckle Reduction and SAR Image Compression Using Multiwavelet Transform

Synthetic aperture radar （SAR） images are corrupted by multiplicative speckle noise which limits the performance of the classical coder/decoder algorithm in spatial domain. The relatively new transform of multiwavelets can possess desirable features simultaneously, such as orthogonality and symmetry, while scalar wavelets cannot. In this paper we propose a compression scheme combining with speckle noise reduction within the multiwavelet framework. Compared with classical set partitioning in hierarchical trees （SPIHT） algorithm, our method achieves favorable peak signal to noise ratio （PSNR） and superior speckle noise reduction performances.

Reference-driven method for MR image reconstruction based on wavelet sparsity and nonlocal total variation

A novel reference-driven method for MR image reconstruction based on wavelet sparsity and nonlocal total variation（NLTV）is proposed.Utilizing the sparsity of the difference image between the target image and the motion-compensated reference image in wavelet transform domain,the proposed method does not need to estimate contrast changes and therefore increases computational efficiency.Additionally,NLTV regularization is applied to preserve image details and features without blocky effects.An efficient alternating iterative algorithm is used to estimate motion effects and reconstruct the difference image.Experimental results demonstrate that the proposed method can significantly reduce sampling rate or improve the quality of the reconstructed image alternatively.

A New DCT Image Compression Algorithm Using Block Visual Activities

A new image compression algorithm is proposed based on local visual activity classification and the investigation of the histograms of the small non-overlapping blocks of the differential and angle image. Histograms of the differential blocks are classified according to their visual activities as Unimodal, Bimodal and Multimodal blocks. According to the histogram shape of the differential block and the mean angle of the same block an optimized quantization table with special coding is applied by taking the advantages of the local visual activities within the block. A considerable compression ratio and visual output improvement compared with the DCT compression algorithm are gained.

A Novel Coding Method Based on Fuzzy Vector Quantization for Noised Image

In this paper a novel coding method based on fuzzy vector quantization for noised image with Gaussian white-noise pollution is presented. By restraining the high frequency subbands of wavelet image the noise is significantly removed and coded with fuzzy vector quantization. The experimental result shows that the method can not only achieve high compression ratio but also remove noise dramatically.