Color Image Compression Research Articles

A color image compression and encryption algorithm combining compressed sensing, Sudoku matrix, and hyperchaotic map

A color image compression and encryption algorithm combining compressed sensing, Sudoku matrix, and hyperchaotic map

Dual-stage feedback network for lightweight color image compression artifact reduction

Lossy image coding techniques usually result in various undesirable compression artifacts. Recently, deep convolutional neural networks have seen encouraging advances in compression artifact reduction. However, most of them focus on the restoration of the luma channel without considering the chroma components. Besides, most deep convolutional neural networks are hard to deploy in practical applications because of their high model complexity. In this article, we propose a dual-stage feedback network (DSFN) for lightweight color image compression artifact reduction. Specifically, we propose a novel curriculum learning strategy to drive a DSFN to reduce color image compression artifacts in a luma-to-RGB manner. In the first stage, the DSFN is dedicated to reconstructing the luma channel, whose high-level features containing rich structural information are then rerouted to the second stage by a feedback connection to guide the RGB image restoration. Furthermore, we present a novel enhanced feedback block for efficient high-level feature extraction, in which an adaptive iterative self-refinement module is carefully designed to refine the low-level features progressively, and an enhanced separable convolution is advanced to exploit multiscale image information fully. Extensive experiments show the notable advantage of our DSFN over several state-of-the-art methods in both quantitative indices and visual effects with lower model complexity.

Neural network models for the quaternion singular value decompositions

We develop two neural network models for computing the quaternion singular value decomposition (QSVD), which can be shown to arise as gradient flows or Riemannian-gradient flows on the product of Stiefel manifold over the quaternion skew-field and their geometric and dynamical properties are investigated. Numerical experiments including color image compression indicate the feasibility and effectiveness of the proposed neural network models.

Graph Based Cross-Channel Transform for Color Image Compression

Adaptive transform coding is gaining more and more attention for better mining of image content over fixed transforms such as discrete cosine transform (DCT). As a special case, graph transform learning establishes a novel paradigm for the graph-based transforms. However, there still exists a challenge for graph transform learning-based image codecs design on natural image compression, and graph representation cannot describe regular image samples well over graph-structured data. Therefore, in this article, we propose a cross-channel graph-based transform (CCGBT) for natural color image compression. We observe that neighboring pixels having similar intensities should have similar values in the chroma channels, which means that the prominent structure of the luminance channel is related to the contours of the chrominance channels. A collaborative design of the learned graphs and their corresponding distinctive transforms lies in the assumption that a sufficiently small block can be considered smooth, meanwhile, guaranteeing the compression of the luma and chroma signals at the cost of a small overhead for coding the description of the designed luma graph. In addition, a color image compression framework based on the CCGBT is designed for comparing DCT on the classic JPEG codec. The proposed method benefits from its flexible transform block design on arbitrary sizes to exploit image content better than the fixed transform. The experimental results show that the unified graph-based transform outperforms conventional DCT, while close to discrete wavelet transform on JPEG2000 at high bit-rates.

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

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

A REAL STRUCTURE-PRESERVING ALGORITHM FOR THE LOW- RANK DECOMPOSITION OF PURE IMAGINARY QUATERNION MATRICES AND ITS APPLICATIONS IN SIGNAL PROCESSING

With the increasing use of quaternions in fields such as quantum mechanics, rigid body rotation, signal and color image processing, and aerospace engineering, three- dimensional signal models represented by pure imaginary quaternion matrices have been cre- ated. This model treats the 3D information as a whole, and the processing preserves the intrinsic connections between the different channels. However, the computational process of quaternion algebra often generates real parts, which are inevitable. How to ensure the pure imaginary properties of 3D signal models is also a research priority. In this paper, we inves- tigate the low-rank decomposition of pure imaginary quaternion matrices using least squares iteration and give a real structure-preserving algorithm for the low-rank decomposition based on the isomorphic real representation. In addition, this matrix decomposition algorithm is applied to color image compression and 3D wave signal denoising problems. Numerical ex- periments show the effectiveness of the algorithm in this paper.

Fuzzy Transform Image Compression in the YUV Space

This research proposes a new image compression method based on the F1-transform which improves the quality of the reconstructed image without increasing the coding/decoding CPU time. The advantage of compressing color images in the YUV space is due to the fact that while the three bands Red, Green and Blue are equally perceived by the human eye, in YUV space most of the image information perceived by the human eye is contained in the Y band, as opposed to the U and V bands. Using this advantage, we construct a new color image compression algorithm based on F1-transform in which the image compression is accomplished in the YUV space, so that better-quality compressed images can be obtained without increasing the execution time. The results of tests performed on a set of color images show that our color image compression method improves the quality of the decoded images with respect to the image compression algorithms JPEG, F1-transform on the RGB color space and F-transform on the YUV color space, regardless of the selected compression rate and with comparable CPU times.

VLSI implementation of image compressor using probabilistic run length coding

AbstractMultimedia applications, such as image processing including image and video transfer, heavily rely on reduction. The traditional device methods to picture reduction use more space, energy, and processing time. The majority of current efforts use the Golomb‐Rice encoding, due to its larger memory requirement and higher computing difficulty. So, this research concentrated on hardware design‐oriented probability run length (PRL) coding technique based on lossless colour image compression. The block truncation coding (BTC) features of the compression process are used by the suggested PRL method. The proposed image compression hardware consists of various modules such as a Parameter calculator, fuzzy table, bitmap generator, BTC parameters training, prediction, and error control, and PRL‐based finite state machine (PRL‐FSM). The proposed image compressor utilizes the parameter calculator block, which estimates the block type based on the image pixel intensities for each sub‐block. Thus, each block of the image is compressed by using a new block type and generates a variable block size. The proposed method utilizes the PRL‐BTC encoding method, which also calculates the probability of error between the compressed image to the test image. The process is iterated until the performance trade‐off between hardware cost and compression ratio (CR) is achieved. Hence, both smooth regions and non‐smooth regions of images are perfectly compressed by the probability‐based block selection. The simulation results show that the proposed method resulted in a better area, power, delay metrics, peak signal‐to‐noise ratio (PSNR), and CR compared to the state of art approaches.

A compressive sensing encryption scheme for dual color images based on discrete memristor map and Rubik’s cube scramble

The most common in practical image encryption are multiple color images, however, less attention is paid to inter-layer correlation broken in existing color image encryption algorithms. In this paper, a 2-D compressive sensing dual color images encryption algorithm based on a discrete memristor hyper-chaotic map and Rubik's cube scramble is proposed. First, we propose a new discrete memristor-Ikeda map (DM-Ikeda), the analysis result shows the DM-Ikeda map has extremely high complexity and hyper-chaotic dynamical behavior. Second, to facilitate ciphertext transmission, the images are compressed and encrypted simultaneously by DM-Ikeda map and 2D compressive sensing. Then, we design a new rubik's cube scramble algorithm, which combines the layers of two images into a cube that can be expanded in different orders, and swaps the pixels of rows, columns or faces of each image by rotating the cube to achieve image scramble. This algorithm can break the correlation not only of pixels within layers of two images but also of pixels between different layers of images. Finally, two images are further diffused with the use of hyper-chaotic sequences. The algorithm can achieve both compression and encryption of dual color images. Experimental results indicate that, our scheme has excellent compression, and can break the correlation between different images and layers, which is excellent resistance to statistical attacks and has high security.

VLSI Design Based on Block Truncation Coding for Real-Time Color Image Compression for IoT.

It has always been a major issue for a hospital to acquire real-time information about a patient in emergency situations. Because of this, this research presents a novel high-compression-ratio and real-time-process image compression very-large-scale integration (VLSI) design for image sensors in the Internet of Things (IoT). The design consists of a YEF transform, color sampling, block truncation coding (BTC), threshold optimization, sub-sampling, prediction, quantization, and Golomb-Rice coding. By using machine learning, different BTC parameters are trained to achieve the optimal solution given the parameters. Two optimal reconstruction values and bitmaps for each 4 × 4 block are achieved. An image is divided into 4 × 4 blocks by BTC for numerical conversion and removing inter-pixel redundancy. The sub-sampling, prediction, and quantization steps are performed to reduce redundant information. Finally, the value with a high probability will be coded using Golomb-Rice coding. The proposed algorithm has a higher compression ratio than traditional BTC-based image compression algorithms. Moreover, this research also proposes a real-time image compression chip design based on low-complexity and pipelined architecture by using TSMC 0.18 μm CMOS technology. The operating frequency of the chip can achieve 100 MHz. The core area and the number of logic gates are 598,880 μm2 and 56.3 K, respectively. In addition, this design achieves 50 frames per second, which is suitable for real-time CMOS image sensor compression.

Quantum color image compression and encryption algorithm based on Fibonacci transform

Quantum color image compression and encryption algorithm based on Fibonacci transform

A Novel Encryption-Then-Lossy-Compression Scheme of Color Images Using Customized Residual Dense Spatial Network

Nowadays it has still remained as a big challenge to efficiently compress color images in the encrypted domain. In this paper we present a novel deep-learning-based approach to encryption-then-lossy-compression (ETC) of color images by incorporating the domain knowledge of the encrypted image reconstruction process. In specific, a simple yet effective uniform down-sampling is utilized for lossy compression of images encrypted with a modulo-256 addition, and the task of image reconstruction from an encrypted down-sampled image is then formulated as a problem of constrained super-resolution (SR) reconstruction. A customized residual dense spatial network (RDSN) is proposed to solve the formulated constrained SR task by taking advantage of spatial attention mechanism (SAM), global skip connection (GSC), and uniform down-sampling constraint (UDC) that is specific to an ETC system. Extensive experimental results show that the proposed ETC scheme achieves significant performance improvement compared with other state-of-the-art ETC methods, indicating the feasibility and effectiveness of the proposed deep-learning based ETC scheme.

Class-associative multiple target recognition for highly compressed color images in a joint transform correlator

We propose a practical near real-time compression-based class-associative multiple target detection technique for color images. The size of the color images is reduced to its utmost ratio to speed up the processing time and to increase the storage capacity of the recognition system. A fringe-adjusted joint transform correlation technique is employed to successfully detect the compression-based multiple targets in colored images. In addition, to eliminate the false alarms and zero-order terms due to multiple desired and undesired objects in an input scene, we have used the shifted phase-encoded and the reference phase-encoded techniques. The performance of detecting the class-associative multiple targets for large compression ratios (up to 94%) and under strong noisy conditions (Gaussian and salt and pepper noises) is assessed through many computer simulations experiments. Furthermore, detecting small targets or even a small part of a target is presented for images occluded up to 90%.

A Multi-Level Enhanced Color Image Compression Algorithm using SVD & DCT

Nowadays, computer technology is mostly concerned with storage capacity and performance. Compression of digital images has become a fundamental aspect of their transmission and storage. Due to storage and bandwidth constraints, it has become necessary to compress images before to transmission and storage. Not only can image compression techniques help to reduce storage space requirements, but they also aid increase transmission bandwidth. Color images are in trend these days during communication. Most of the researchers have worked only on grayscale images. This research proposes a hybrid approach that encompasses two cutting-edge picture compression algorithms: DCT & SVD. This research involves the advantages and strength of two cutting-edge picture compression algorithms that enable us to compress the color images without additional cost in computation, space and time. Here in this research, for experimental purposes, seam carving image dataset is used. The proposed method's performance is evaluated using the performance evaluation matrices, i.e., Size after Compression, MSE, PSNR, Normalized Co-relation (NC), Percentage Space-Saving, and Compression Ratio. The proposed method performance is also correlated with the two latest image compression techniques, i.e., DCT Block Truncation (DCTBT) and Discrete Cosine Transform - Vector Quantization (DCT-VQ). The findings show that the suggested hybrid color image compression approach is superior to existing compression according to different performance metrics.

Securing data using deep hiding selected least significant bit and adaptive swarm algorithm

The emphasis on data protection is improved in particular with respect to the transmission protocols utilized. Different research on numerous data protection areas such as authentication, encryption, hiding of data and validation were performed. In addition, a cybersecurity standard, such as IP-SEC, and secure sockets layer (SSL), were introduced to solve privacy infringement problems by applying encryption, authorization and protection to data exchanged and data stored in the cloud. This study suggests a new steganography algorithm, a data protection tool used to conceal massive amounts of data from graphic and statistic attacks in color images. The proposed algorithm is a multi-level steganography modified deep hiding/extracting technique (MDHET), which implements a selected least signified bit (SLSB) for color picture dispersal of the information. In addition, an accurate pixel location randomization feature has been applied. After MDHET, the predicted results will effectively conceal data up to 6 bpp (bit per pixel) with high safety levels by improving the quality of images. In addition, MDHET can be useful for encoding a deep series of images into one in which the testing procedure is carried out using regular reference images used in color image processing and compression analysis from different institutions.

NEURAL NETWORKS TRAINING BASED ON RANDOM SEARCH

The paper deals with a state-of-art problem, associated with neural networks training. Training algorithm (with special parallelization procedure) implementing the annealing method is proposed. The training efficiency is demonstrated by the example of a neural network architecture focused on parallel data processing. For the color image compression problem, it is shown that the proposed algorithm significantly outperforms gradient
 methods in terms of efficiency. The results obtained make it possible to improve the neural networks training quality in general, and can be used to solve a wide class of applied problems.

General Image Fast Encryption Algorithm for Deep Learning Compression and Reconstruction

Aiming at the problems of long time-consuming in image compression encryption and color distortion of the decrypted image, a fast and safe general image encryption algorithm is proposed. The compression network uses bilinear interpolation and a fully connected layer for compression preprocessing to prevent the loss of color information. Convolution layer improves compression rate and reduces time complexity in this algorithm. In the encryption part, the chaotic sequence dynamically selects the pixel block and the pixel points in the block combining the XOR operation to perform pixel diffusion, which can improve the randomness of the algorithm. In the improved bilinear convolution neural network, bilinear and convolutional layers are used to obtain the image contour features. And the fully connected layer obtains color information for image reconstruction. This algorithm realizes the integration of compression, encryption, decryption and restoration of grayscale and RGB format color images. The experimental results show that the various encryption indexes of this algorithm are close to ideal values. It has good encryption security and anti-attack ability. Moreover, the encryption time is greatly shortened. On the 500 test set (BSD500) commonly used in image processing, when the sampling rate is 0.25, 0.10, 0.04 and 0.01, the peak signal-to-noise ratio can reach to 29.01 dB, 25.69 dB, 23.75 dB and 21.45 dB, which is better than FCLBCNN. This encryption algorithm effectively solves the color distortion problem of reconstructed images and has better encryption effects and wider applicability.

Detecting Aligned Double JPEG Compressed Color Image With Same Quantization Matrix Based on the Stability of Image

Joint photographic experts group (JPEG) compression is widely used in image processing and computer vision. Detecting double compressed JPEG images is a common problem in forensics and detecting compressed images with the same quantization matrix remains a challenging task. However, most existing methods were designed for detection in grayscale images and cannot fully use the unique characteristics of color images (such as the relationship between channels and color information). In addition, the performance of existing methods is unsatisfactory for low JPEG quality factors and in cross detection experiments. To solve these problems, we analyze the stability of a color image to obtain the convergence error and transposition error. According to the convergence characteristics of color JPEG images, the continuous compression by the same quantization matrix can make the JPEG image tend to be stable. The final stable state and the convergence process are determined by the number of compressions of the original image. Thus, continuously compressed JPEG images can be regarded as a continuous frame to obtain the convergence error. As the color image converges, its ability to resist interference decreases. To reflect the changes in anti-interference ability, the transposition operation is used to disturb the color JPEG image to obtain the transposition error. In addition, quaternion mapping is used to retain the relationship between continuously compressed JPEG images and enlarge the influence caused by transposition operation. In our experiments on several image databases, the proposed method outperforms existing methods in different settings.

New Sampling Expansion Related to Derivatives in Quaternion Fourier Transform Domain

The theory of quaternions has gained a firm ground in recent times and is being widely explored, with the field of signal and image processing being no exception. However, many important aspects of quaternionic signals are yet to be explored, particularly the formulation of Generalized Sampling Expansions (GSE). In the present article, our aim is to formulate the GSE in the realm of a one-dimensional quaternion Fourier transform. We have designed quaternion Fourier filters to reconstruct the signal, using the signal and its derivative. Since derivatives contain information about the edges and curves appearing in images, therefore, such a sampling formula is of substantial importance for image processing, particularly in image super-resolution procedures. Moreover, the presented sampling expansion can be applied in the field of image enhancement, color image processing, image restoration and compression and filtering, etc. Finally, an illustrative example is presented to demonstrate the efficacy of the proposed technique with vivid simulations in MATLAB.

Color Image Compression Scheme with Reduced Computational Complexity

Color Image Compression Scheme with Reduced Computational Complexity