Color Image Compression Research Articles

Color Image Compression Scheme with Reduced Computational Complexity

Color Image Compression Scheme with Reduced Computational Complexity

Multiple color image fusion, compression, and encryption using compressive sensing, chaotic-biometric keys, and optical fractional Fourier transform

A new multiple color image fusion, compression, and encryption using compressive sensing, chaotic-biometric keys, and optical fractional Fourier transform is proposed. In the proposed cryptosystem, the original four-color images are independently decomposed into four sub-bands by a 2-D discrete wavelet transform. The corresponding four LL sub-bands are fused to construct a single fused image, which is split into R, G, and B channels. Each channel is compressed by using compressive sensing (CS). The measurement matrix is constructed as a circulant matrix based on the random sequence generated with a logistic map, which is modulated by the compressed R and G channels of palmprint to produce the first chaotic- biometric phase mask (CBPM) and by the compressed B channel of palmprint to produce the second CBPM. The compressed R and G channels of fused image are encoded in a complex image, modulated by the first CBPM, and then fractional Fourier transformed. The transformed image is phase- and amplitude-truncated to obtain the first encrypted image and first decryption key, respectively. The first encrypted image and the compressed B channel are encoded in a complex image, modulated by the second CBPM, and then fractional Fourier transformed (FrFT). The resulting image is phase- and amplitude-truncated to obtain a final encrypted image and second decryption key, respectively. The proposed compressed single-channel cryptosystem has advantages of reduced date storage to be transmitted, uniqueness of biometric keys in CBPMs, very sensitive orders of the FrFT, and a single-channel hybrid optoelectronic setup. The proposed cryptosystem is highly secure against special attacks. Numerical simulation results validate the viability and security of the proposed system.

Segment Based Compressive Sensing (SBCS) of Color Images for Internet of Multimedia Things Applications

Telemedicine is one of the IoMT applications transmitting medical images from hospital to remote medical centers for diagnosis and treatment. To share this multimedia content across internet, storage and transmission become a challenge because of its huge volume. New compression techniques are being continuously introduced to circumvent this issue. Compressive sensing (CS) is a new paradigm in signal compression. Block based compressive sensing (BCS) is a standard and commonly used technique in color image compression. However, BCS suffers from block artifacts and during transmission, mistakes can be introduced to affect the BCS coefficients, degrading the reconstructed image’s quality. The performance of BCS at low compression ratios is also poor. To overcome these limitations, without dividing the image into blocks, the image matrix is considered as a whole and compressively sensed by segment based compressive sensing (SBCS). This is a novel strategy that is offered in this article, for efficient compression of digital color images at low compression ratios. Metrics of performance The peak signal to noise ratio (PSNR), the mean structural similarity index (MSSIM), and the colour perception metric delta E are computed and compared to those obtained using block-based compressive sensing (BBCS). The results show that SBCS performs better than BBCS.

Effect of Channel Consideration on Auto Encoders for Color Image Compression using Deep Learning

In this paper, we have studied the effect of channels consideration on autoencoders for color image compression. The study is made in relation to RGB patch in an image and individual channel patches to know the effectiveness of what criteria is to be used while processing the image for compression. The study reveals that the RGB patch consideration in a color image is better than considering the channels individually. The chaotic (or scramble) image is given as input to autoencoder for compression and this helps to overcome the threat by the intruder and as well protection to data transmitted.

Art Image Compression Based on Lossless LZW Hashing Ciphering Algorithm

Color image compression is a good way to encode digital images by decreasing the number of bits wanted to supply the image. The main objective is to reduce storage space, reduce transportation costs and maintain good quality. In current research work, a simple effective methodology is proposed for the purpose of compressing color art digital images and obtaining a low bit rate by compressing the matrix resulting from the scalar quantization process (reducing the number of bits from 24 to 8 bits) using displacement coding and then compressing the remainder using the Mabel ZF algorithm Welch LZW. The proposed methodology maintains the quality of the reconstructed image. Macroscopic and quantitative experimental results on technical color images show that the proposed methodology gives reconstructed images with a high PSNR value compared to standard image compression techniques.

Color image compression based on spatial and magnitude signal decomposition

<p>In this paper, a simple color image compression system has been proposed using image signal decomposition. Where, the RGB image color band is converted to the less correlated YUV color model and the pixel value (magnitude) in each band is decomposed into 2-values; most and least significant. According to the importance of the most significant value (MSV) that influenced by any simply modification happened, an adaptive lossless image compression system is proposed using bit plane (BP) slicing, delta pulse code modulation (Delta PCM), adaptive quadtree (QT) partitioning followed by an adaptive shift encoder. On the other hand, a lossy compression system is introduced to handle the least significant value (LSV), it is based on an adaptive, error bounded coding system, and it uses the DCT compression scheme. The performance of the developed compression system was analyzed and compared with those attained from the universal standard JPEG, and the results of applying the proposed system indicated its performance is comparable or better than that of the JPEG standards.</p>

Implementation Of A Hybrid Color Image Compression Technique Using Principal Component Analysis And Discrete Tchebichef Transform

The proliferation of scientific and technological demands of high-resolution multimedia contenthas extremely increased the data volume in enterprise data centers and servers on the internet.The characterization of high-resolution picturescreates a great challenge to transfer files with the immensely colossal volume of image data over communication networks. The uploading/downloading time of large images has always been a keyproblemon the Internet. In addition to data communicationissues, high-resolution photo consumes larger storage capacity. Therefore, compression is an almost inevitable process towards the reduction of the transmission time and/or storage capacity requirements of images.Principle component analysis (PCA) and Discrete Tchebichef Transform (DTT) algorithmsare often employed for image compression in several references. In this paper, we propose a hybrid color image compression approachbased on PCA and DTT algorithms (PCADTT), which integrates the benefits of both PCA and DTT algorithms.Our hybrid approach exploits (i) PCA to reduce the dimensionality of the image; and (ii) DTT algorithm to enhance the image quality.The proposed technique has been assessed and related to a compression method that integrates DTT with a singular value decomposition (SVD) scheme (DTTSVD)using different performancemeasures including compression time (CT),compression ratio (CR), structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and universal quality index (UQI). The experimental results reveal that our proposed method outperformsthe existing method for all kinds of image content at a high compression ratio with lower computational complexity and retaining thequality of imagedata.

A new, enhanced EZW image codec with subband classification

In this paper, an enhanced version of Embedded zerotree wavelet (EZW) image coding algorithm is proposed, referred to as EZW-SC. By exploiting a new principle that relies on a subband classification concept, the enhanced algorithm allows the prediction of insignificant subbands at early passes, along with the use of an improved significance map. This reduces the redundancy of zerotree symbols, speeds up the coding process and improves the coding of significant coefficients. In fact, the EZW-SC algorithm scans only significant subbands and significantly improves the lossy compression performance with the conventional EZW. Moreover, new EZW-based schemes are presented to perform colour image coding by taking advantage of the interdependency of the colour components. Experimental results show clear superiority of the proposed algorithms over the conventional EZW as well as other related EZW schemes at various bit rates in both greyscale and colour image compression.

Exploiting 2D compressed sensing and information entropy for secure color image compression and encryption

Compared to 1D compressed sensing (CS), 2D CS is more efficient for compressing the plaintext image from two directions, but security level of current 2D CS-based ciphers is unsatisfactory. To solve this problem, this paper presents a novel color image compression and encryption algorithm by combining 2D CS, information entropy and chaos. Firstly, the color image is decomposed into red, green and blue components, then they are sparsely transformed by the discrete wavelet transform (DWT) to get three sparse matrices. Next, the obtained matrices are observed by two asymptotical deterministic random measurement matrices based on information entropy and counter (ADMMIC), which not only encrypts the plaintext image, but also compresses it in proportion to reduce the transmission bandwidth and storage space. Subsequently, the corresponding measurement value matrices are shuffled by a double random scrambling based on Arnold map and index vector (DRSAIV) to eliminate the correlation between adjacent pixels. Furthermore, the obtained permutated matrices are diffused by a simultaneous multiple random diffusion of inter–intra components (SMRDIC) to obtain the final cipher image, the plaintext pixel to be diffused, the key matrix involved in diffusion and the position of the obtained ciphertext pixel are all unpredictable, which makes statistical attack invalid. In addition, information entropy values of plaintext image are obtained to generate the initial values of the used chaotic systems, which greatly improve the ability to resist the known-plaintext and chosen-plaintext attacks. Simulation results and security analyses verify that this algorithm has good compression and high security.

Data Hiding Method for Color AMBTC Compressed Images Using Color Difference

Image compression technology and copyright protection are certainly the important technologies for free exchange of multimedia. For compression of an image, we propose a color Absolute Moment Block Trucation Coding (AMBTC) method using a common bit-plane created by k-means. In addition, a data hiding method based on a color AMBTC using Optimal Pixel Adjustment Process (OPAP) was proposed for copyright protection and confidential secret communication. The number of quantization levels of the proposed color AMBTC is nine per block. Therefore, the edge of the compressed color image can be expressed more delicately. As a result of the simulation, it can be seen that the edge of the image of the color AMBTC is close to the original image. Moreover, the data hiding performance of the proposed method also obtained excellent results. For the experiment, we measured the quality of the image using the Color Difference (CD) we proposed, and the measurement result was very satisfactory.

Research on blur discrimination thresholds of three-Channel for color image

On the basis of modern theory of color vision, the blur discrimination threshold of color image with three channels (luminance, red-green and the yellow-blue channel) in the opponent color space (DKL space) is studied through visual experiments in this paper. The visual evaluation experiment is designed by the psychophysical method based on Quest algorithm. Finding the blur discrimination threshold of color image according to the DKL space is of great significance for helping us understand color perception of image. It also has a wide range of applications in areas such as color image compression during color image transmission. Two (flowers and artificial object) typical images in the McGill standard color image database were chosen as the experimental images. After converting the color space of the image from RGB to DKL space, Gaussian low-pass filtering is used to establish the fuzzy image database. Quest algorithm which is a psychological physical method is used to design the visual evaluation experiment. Eleven volunteers with normal color vision were invited to participate in the visual evaluation experiment for each of the three channels of the two images. Our main conclusions are: (1) Human eyes are more sensitive to luminance information than color information. (2) The larger the contrast of the image and the smaller the uniformity of the image, the more sensitive the human eye is to changes in image blur.

The Application of Color Image Compression Based on Discrete Wavelet Transform

The rapid progress in technology has been the use of color images that contain very large data and to transfer large information without loss of image information led to the creation of an important tool used to compress the color image, which is the modern wavelengths derived from polynomials, depending on the influencers (s,r) responsible for expansion, contraction and compensation in The parent function for obtaining the functions at each point depends on the influences responsible for the formation of the wavelet functions from which we derive the appropriate filter responsible for analyzing the color image and divide it into parameters that divide into two parts Approach and Details. In this work, new wavelets were used in order to find the exact solution to a number of important numerical problems in the field of mathematics. These methods are tested in the field of multi resolution analysis (MRA) for using in the field of image processing where it was used in the analysis and compression of the color image, calculating the most important image quality Mean Square Error (MSE) and Peak signal-to-noise ratio (PSNR). Standards and comparing the results with the results obtained using standard wavelets used in the Matlab program that was used in this work. In physics, the images of atoms were taken, random images were designed and designed with a size (256 x256) that were processed using modern wavelets.

Тестирование методов сжатия изображений

Seven console codecs BMF 2.01 (BMF.exe), WebP (cwebp.exe, dwebp.exe), PAQ8L (paq-8l_intel.exe), PAQ8P (paq8p_sse2.exe), JPEG-LS (jpeg.exe), JPEG2000 (convert.exe), PNG (opting.exe) are tested on a set of 19 grayscale images and 10 color images (widespread images used in testing compression methods). An image compression program tester was developed. The tester receives images and executable files of image compression programs, and programs are started for each input image to compress and restore the image. The program operation results are contained in an HTML/CSS file, which includes, among other information, the bitrates achieved by the compression programs and the results of checking how successfully the compressed files were restored. Partial clones of the Blend-A13+, Blend-16, Blend-20 compression methods have been made to compare the effectiveness of the multipredictors that lie at the heart of the Blend-A13+, Blend-16, Blend-20 methods. Partial clones of the Blend-A13+, Blend-16, Blend-20 compression methods consist of multipredictors used in the Blend-A13+, Blend-16, Blend-20, methods, and 13 and 16 elementary predictors used in the Blend-A13+ and Blend-16 methods, respectively. The predictor GAP+ is replaced by the predictor GAP; the modeling like JPEG-LS is replaced by contextual modeling with quantization of a context from differences of pixels from the vicinity of the coded pixel, the arithmetic coder, and the reversible intercolor RGB-YUV transformation from JPEG2000. For many images, the obtained partial clones outperformed the results yielded by the JPEG-LS, JPEG2000, PNG methods and gave results at the level provided by the PAQ8L and WebP compression methods. In general, the BMF2.01 method demonstrated the best results on the test set of images. On the test set of images, the multipredictors from Blend-16 and Blend-20 unexpectedly provided color image compression results poorer than the multipredictor from the Blend-A13+ method. In compressing grayscale images, the multipredictor from Blend-20 yielded better results than the multipredictors from Blend-16, Blend-A13+.

Color Image Compression System by using Block Categorization Based on Spatial Details and DCT Followed by Improved Entropy Encoder

In this paper, a new high-performance lossy compression technique based on DCT is proposed. The image is partitioned into blocks of a size of NxN (where N is multiple of 2), each block is categorized whether it is high frequency (uncorrelated block) or low frequency (correlated block) according to its spatial details, this done by calculating the energy of block by taking the absolute sum of differential pulse code modulation (DPCM) differences between pixels to determine the level of correlation by using a specified threshold value. The image blocks will be scanned and converted into 1D vectors using horizontal scan order. Then, 1D-DCT is applied for each vector to produce transform coefficients. The transformed coefficients will be quantized with different quantization values according to the energy of the block. Finally, an enhanced entropy encoder technique is applied to store the quantized coefficients. To test the level of compression, the quantitative measures of the peak signal-to-noise ratio (PSNR) and compression ratio (CR) is used to ensure the effectiveness of the suggested system. The PSNR values of the reconstructed images are taken between the intermediate range from 28dB to 40dB, the best attained compression gain on standard Lena image has been increased to be around (96.60 %). Also, the results were compared to those of the standard JPEG system utilized in the “ACDSee Ultimate 2020” software to evaluate the performance of the proposed system.

Optimized color space for image compression based on DCT and Bat algorithm

This paper develops an efficient color image compression method based on the DCT and a new color base. Digital color images are commonly represented in RGB space. Generally, it is noted that a strong correlation exists between the three planes R, G, and B of a color image. The reduction of this correlation certainly offers an advantage in the compression of RGB images. In this context, there is an infinite number of possible spaces to represent the RGB image. The main contributions of this paper are summarized in two main points. First, we design an optimized color space B1B2B3 using the Bat algorithm (BA) to pass from the RGB space to space more appropriate for each image. This space is produced by maximizing the energy of the image in the plane B1 more than in B2 and B3. Second, we produce optimized thresholds appropriate to each plane of the converted image. The Bat algorithm optimizes the cost function to compute thresholds to partially reduce the number of the less significant DCT coefficients that correspond to the lower quantity of energy. The reported results against those of recent methods prove that the proposed method presents high performances in terms of peak signal to noise ratio (PSNR) on the commonly used test color images as well as the test medical images in literature.

Asymmetric color image encryption and compression based on discrete cosine transform in Fresnel domain

In this paper, a new asymmetric method is presented to encrypt and compress color images using discrete cosine transform in Fresnel domain. First, the input color image is decomposed into three channels: red, green and blue. Each channel image is converted into phase image and bonded with an amplitude mask followed by Fresnel transform to get the complex intermediate image. Phase reservation (PR) and phase truncation (PT) operation are performed on the intermediate image. Thereafter, a random phase mask is bonded with the amplitude part and the phase part serves as of the first private key for decryption. The complex image is Fresnel propagated followed by the second PR and PT operation. Phase part serve as another private key and the discrete cosine transform is applied on the amplitude part of each red, green and blue channel to get the corresponding compressed encrypted images. The final compressed color encrypted image is obtained by combining the encrypted images of all three channels. In the decryption process, encrypted image is decomposed in red, green and blue channel and lost data in each channel is replaced by zeros. Each channel is subject to inverse discrete cosine transform and cascaded PT/PR operations with Fresnel propagation in reverse directions and recombined to get the original image. Numerical simulation results are presented to validate the proposed method. Also, the robustness of the proposed method is checked against various existing attacks and a comparative analysis confirms the effectiveness of our method.

Secured Color Image Compression based on Compressive Sampling and Lü System

An efficacious and unharmed approach is vital for transmission of sensitive and secret images over unsecure public Internet. In this paper, secured color image compression method based on compressive sampling and Lü system is proposed. Initially, the plain-image is sparsely represented in transform basis. Compressive sampling measurements are obtained from these sparse transform coefficients by employing incoherent sensing matrix. To upgrade the security level, permutation-substitution operations are performed on pixels based on Lü system. To concoct input sensitivity in the scheme, the keys are obtained from input image. Lastly, fast and efficient greedy algorithm is utilized for sparse signal reconstruction. To evaluate the performance of the proposed scheme Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), Average Difference (AD), Structural Content (SC), Normalized Cross Correlation (NCC), Normalized Absolute Error (NAE), Edge Strength Similarity (ESSIM), Maximum Difference (MD), Correlation Coefficient, Unified Average Changing Intensity (UACI), Key Sensitivity, Number of pixel change Rate (NPCR), Key Space and Histogram metrics are used. Experimental results demonstrate that the proposed scheme produced highly satisfactory security.

Color image compression and encryption scheme based on compressive sensing and double random encryption strategy

Based on compressive sensing and double random encryption strategy, a novel color image compression and encryption scheme is proposed in this paper. The architecture of compression, confusion and diffusion is adopted. Firstly, the red, green and blue components of color plain image are converted to three sparse coefficient matrices by discrete wavelet transform (DWT), and then a double random position permutation (DRPP) is introduced to confuse the coefficient matrices. Subsequently, Logistic-Tent system is utilized to generate the asymptotic deterministic random measurement matrix based on chaotic system and plain image (ADMMCP), which is used to measure the coefficient matrices to obtain measurement value matrices. Moreover, simultaneous double random pixel diffusion between inter-intra components (SDRDIC) is presented to modify the elements of measurement value matrices to obtain the final cipher image. A 4-D hyperchaotic system is applied to produce chaotic sequences for confusion and diffusion, the initial conditions of the used chaotic systems are controlled by the SHA 512 hash value of plain image and external keys, such that the proposed image cryptosystem may withstand known-plaintext and chosen-plaintext attacks. Experimental results and security analyses verify the effectiveness of the proposed cipher.

Build receptive pyramid for efficient color image compression artifact reduction

A traditional image pyramid is an effective way to extract multiscale features. However, for image restoration, downsampling and upsampling lose details in the original resolution and result in an over smooth result. To overcome this defect, we propose a receptive pyramid (RP) that replaces downsampling by dilated convolution to extract multiscale features on the original resolution and make a full resolution correction. We present an RP-based convolution neural network named receptive pyramid convolutional network (RPCN) for efficient color image compression artifact reduction (CAR). Specifically, we propose residual connected convolution blocks as the baseline of RPCN. RPs work in convolution blocks to extract the hierarchical multiscale features for local feature correction. Moreover, the global feature fusion and vector correction are also introduced to further exploit the hierarchical features from the baseline. Benefiting from the RP, our RPCN achieves state-of-the-art performance with a much smaller model and much faster running speed for the CAR task.

Color Image Compression using JPEG and DCT with Blocking and Subsampling

Color Image Compression using JPEG and DCT with Blocking and Subsampling