Image Compression Algorithm Research Articles

A Plant Image Compression Algorithm Based on Wireless Sensor Network

This paper designs and implements an image transmission algorithm applied to plant information collection based on the wireless sensor network. It can effectively reduce the volume of transmitted data, low-energy, high-availability image compression algorithm. This algorithm mainly has two aspects of improvement measures: the first is to reduce the number of pixels that transmit images, from interlaced scanning to interlaced neighbor scanning; the second is to use JPEG image compression algorithm [1], changing the value of the quantization table in the algorithm [2]. After image compression, the image data volume is greatly reduced; the transmission efficiency is improved; and the problem of excessive data volume during image transmission is effectively solved.

Lossless CFA Image Compression Chip Design for Wireless Capsule Endoscopy

This paper presents a hardware-oriented lossless color filter array (CFA) image compression algorithm for very-large-scale integration (VLSI) circuit design. In order to achieve high performance, low complexity and low memory requirement, a novel lossless CFA image compression algorithm based on JPEG-LS is proposed for the VLSI implementation. A previous study showed the usage of a context table with its memory consuming more than 81% of the chip area for a JPEG-LS encoder design. The proposed algorithm implements a JPEG-LS-based lossless image compression algorithm that eliminates the use of the context technique and its memory in order to reduce the chip area while still maintaining its high performance. The proposed algorithm includes a pixel restoration, an adaptive Golomb-Rice parameter prediction and an improved Golomb-Rice coding technique. This paper was realized using a 0.18μ m CMOS process with synthesized gate counts and core area of 4.8 k and 57,625μm 2 , respectively. The synthesized operating frequency of this design reached 200 MHz by using a pipeline scheduling technique. Compared with the previous JPEG-LS-based designs, this paper reduced the gate count to at least 28% and increased the average compression ratio by over 17.15% using the video endoscopy images from the Gastro Gastroenterologist Hospital.

Selection of Vanishing moments of Symlet and Coiflet Wavelets on CT Images

The main aim of the work is to develop image compression algorithms with high quality and compression ratio. The objective also includes finding out an best algorithm for medical image compression techniques. The objective is also alert towards the choice of the developed image compression algorithm, which do not modify the characterization behavior of the image. In this paper, image compression algorithm based on discrete symlet and coiflet wavelet transform is implemented for decomposing the image. The selection of different vanishing moments are discussed based on the values of peak signal to noise ratio (PSNR), compression ratio (CR), means square error (MSE) and bits per pixel (BPP). The optimum moments for compression are also chosen based on the results.

UAV Image High Fidelity Compression Algorithm Based on Generative Adversarial Networks Under Complex Disaster Conditions

This paper proposes an improved image high fidelity compression algorithm based on the generative adversarial networks (GANs) to deal with the problem that the UAV image has a large amount of data which is not conducive to post-processing. By adding an encoder in front of the generator, the disaster area image transmitted by UAV is compressed to meet the requirements of the generator. After the compressed image is trained together with the real image through the discriminator, the quality of the compressed image is constantly improved. This image compression algorithm can fully synthesize the codes of non-major areas such as trees and rivers in the image, and try to retain the codes of important areas such as houses and roads. The experimental results show that the proposed compression method in this paper has a higher compression ratio than the traditional compression method for the disaster area image, and can obtain images with strong sense of hierarchy.

An Effective Algorithm and Architecture for the High-Throughput Lossless Compression of High-Resolution Images

This paper proposes a high-throughput lossless image-compression algorithm based on Golomb–Rice coding and its hardware architecture. The proposed solution increases compression ratios (CRs) while preserving the throughput by taking advantage of a novel parallel variable-length sign coding (PVSC) algorithm that reduces the sign bits to achieve a higher CR. In addition, the proposed solution adopts and modifies the two existing compression algorithms to improve the overall compression performance. The experimental results show that the proposed solution yields an average CR of 3.12, which is higher than those achieved with the previous algorithms. The hardware implementation of the proposed solution for an $8 \times 8$ block unit achieves a throughput of 18 GBps and 24 GBps when encoding and decoding, respectively. This hardware performance is enough to handle $7680\times 4320$ @240-Hz image processing.

A Fast Fractal Based Compression for MRI Images

Magnetic resonance imaging (MRI), which assists doctors in determining clinical staging and expected surgical range, has high medical value. A large number of MRI images require a large amount of storage space and the transmission bandwidth of the PACS system in offline storage and remote diagnosis. Therefore, high-quality compression of MRI images is very research-oriented. Current compression methods for MRI images with high compression ratio cause loss of information on lesions, leading to misdiagnosis; compression methods for MRI images with low compression ratio does not achieve the desired effect. Therefore, a fast fractal-based compression algorithm for MRI images is proposed in this paper. First, three-dimensional (3D) MRI images are converted into a two-dimensional (2D) image sequence, which facilitates the image sequence based on the fractal compression method. Then, range and domain blocks are classified according to the inherent spatiotemporal similarity of 3D objects. By using self-similarity, the number of blocks in the matching pool is reduced to improve the matching speed of the proposed method. Finally, a residual compensation mechanism is introduced to achieve compression of MRI images with high decompression quality. The experimental results show that compression speed is improved by 2-3 times, and the PSNR is improved by nearly 10. It indicates the proposed algorithm is effective and solves the contradiction between high compression ratio and high quality of MRI medical images.

Pixel-based Character Image Compression for Data Transfer from ARM Controller to FPGA System

This paper proposes a pixel-based compression algorithm for character digital image in improving the storage of characters in memory during system operation. In particular, in this algorithm, each character binary image in text is grouped by binary numbers and then encoded to reduce the character image capacity of the character compared to the original character. In addition, a novel point in this algorithm is that one character image type is differently grouped binary numbers for compressing. Therefore, the compressed character image is stored in a memory using an ARM microcontroller system and transferred to an FPGA module for decoding before printing. Moreover, the compression ratio of each character is high or low depending on the font type of image characters. Therefore, the high compression ratio using this compression algorithm will allow saving memory space in the memory system. Simulation results show to illustrate the effectiveness of the proposed algorithm and also this compression algorithm was implemented to texts with characters for encoder data transfer from an ARM microcontroller into an FPGA system for effectively printing the text/logo/barcode/QR code/expired date on products with high speed after decoding. Moreover, this compression algorithm can be developed to apply to many different font types and sizes, as well as be utilized different microcontrollers/Microprocessors connected to FPGA systems for processing with high speed. It means that one industrial system using this algorithm can obtain very high performance related to processing digital image characters.

Image compression and encryption algorithm based on advanced encryption standard and hyper-chaotic system

Image compression and encryption algorithm based on advanced encryption standard and hyper-chaotic system

Rebuilding of Image Compression Algorithm Based on the DCT (discrete cosine transform)

Rebuilding of Image Compression Algorithm Based on the DCT (discrete cosine transform)

Research on fractal image compression hybrid algorithm based on convolutional neural network and gene expression programming

Regarding the problems of insufficient image segmentation intelligence, low compression rate, slow speed for global searching to find the optimal fractal image compression encoding, and bad decoding effect, this article proposes the fractal image compression hybrid algorithm based on convolutional neural network and gene expression programming. Firstly, according to the accurate and fast image classification of deep convolutional neural network and the fast search and matching encoding advantages of gene expression programming, it realizes theoretically the action mechanism of fractal image compression hybrid encoding by combining the convolutional neural network and the gene expression programming; then, it uses the deep convolutional neural network to train and classify the image, and uses the adaptive quadtree segmentation method to segment the classified image, thus generating the domain block and range block classification set. According to the action mechanism of gene expression programming in fractal image compression encoding, it then quickly obtains the optimal solution of fractal image compression encoding by searching and encoding the sub-blocks of range block classification set and the classification set corresponding to the domain. Finally, in the CPU/GPU environment, it conducts the comparative experiment with basic fractal image compression algorithm and fractal image compression algorithm based on convolutional neural network. The experimental results show that this proposed algorithm outperforms similar algorithms in terms of image segmentation speed and accuracy as well as fractal compression encoding speed and compression ratio. Therefore, this algorithm is a fractal image compression algorithm with intelligent segmentation, fast encoding and high compression ratio.

Run Length Encoding Based Lossless MRI Image Compression Using LZW and Adaptive Variable Length Coding

Medical image compression is considered one of the most important research fields nowadays in biomedical applications. The majority of medical images must be compressed without loss because each pixel information is of great value. With the widespread use of applications concerning medical imaging in the health-care context and the increased significance in telemedicine technologies, it has become crucial to minimize both the storage and bandwidth requirements needed for archiving and transmission of medical imaging data, rather by employing means of lossless image compression algorithms. Furthermore, providing high resolution and image quality preservation of the processed image data has become of great benefit. The proposed system introduces a lossless image compression technique based on Run Length Encoding (RLE) that encodes the original magnetic resonance imaging (MRI) image into actual values and their numbers of occurrence. The actual image data values are separated from their runs and they are stored in a vector array. Lempel–Ziv–Welch (LZW) is used to provide further compression that is applied to values array only. Finally the Variable Length Coding (VLC) will be applied to code the values and runs arrays for the precise amount of bits adaptively into a binary file. These bit streams are reconstructed using inverse LZW of the values array and inverse RLE to reconstruct the input image. The obtained compression gain is enhanced by 25% after applying LZW to the values array.

Phase-difference-based compression of phase-only holograms for holographic three-dimensional display.

In holographic three-dimensional (3D) display, holograms for reconstructing 3D scenes require huge storage space and high transmission bandwidth. Holographic data must be compressed for practical applications. The holograms for 3D display are commonly represented in pure-phase format. Existing hologram compression techniques are generally not designed to handle phase-only holograms. In this paper, we propose a phase-difference-based compression method to compress phase-only holograms for holographic 3D display. Phase-only holograms are decomposed into grayscale images representing the phase distance and binary images containing the sign information of the phase difference. The grayscale images can be better handled by common image compression algorithms since their pixel values are proportional to the phase distance reflecting the distance between complex amplitudes. The binary images are compressed by lossless bi-level image coding. The compressed grayscale images and binary images can be synthesized to recover the phase-only holograms and reconstruct the 3D scenes. The advantages of the proposed method over existing image coding standards are verified by numerical simulations.

Review and evaluation of color spaces for image/video compression

AbstractA color space plays an important role in color image processing and color vision applications. While compressing images/videos, properties of the human visual system are used to remove image details unperceivable by the human eye, appropriately called psychovisual redundancies. This is where the effect of the color spaces' properties on compression efficiency is introduced. In this work, we study the suitability of various color spaces for compression of images and videos. This review work is undertaken in two stages. Initially, a comprehensive review of the published color spaces is done. These color spaces are classified and their advantages, limitations, and applications are also highlighted. Next, the color spaces are quantitatively analyzed and benchmarked in the perspective of image and video compression algorithms, to identify and evaluate crucial color space parameters for image and video compression algorithms.

An Improved Image Compression Algorithm Using Wavelet and Fractional Cosine Transforms

An Improved Image Compression Algorithm Using Wavelet and Fractional Cosine Transforms

Energy efficient image coding techniques for low power sensor nodes: A review

Abstract Visual Sensors Networks (VSN) are spatially dispersed distributed networks, consisting of small sensing units and image sensors. They are scattered over a region to sense, collect and transfer data and are involved in domains such as environmental monitoring, surveillance and tracking. The resource restrictions imposed on sensor nodes are the challenges for image transmission. Sensor nodes are battery power supplied. The greatest operative solution is image compression for energy efficient image communication. With the advent of VSNs, energy-aware compression algorithms have gained wide attention. Since the application of the conventional standards are not energy beneficial. New strategies and mechanisms for power-efficient image compression algorithms are developed. The scope of this review is to provide a holistic review of such energy efficient image compression algorithms for camera equipped VSN. This survey enumerates the benefits and limitations of conventional image compression standards to latest compression technique developed and adapted for VSN.

Fuzzy c-Means Based Adversarial Whale Optimization Algorithm for Compound Image Compression

Fuzzy <i>c</i>-Means Based Adversarial Whale Optimization Algorithm for Compound Image Compression

Backpropagation Approach Supported by Image Compression Algorithm for the Classification of Chronic Condition Diseases.

Diabetes is one of the main public health chronic conditions that are potentially reaching epidemic proportions globally. Worldwide, the occurrence of these types of diseases are increasing sharply at a worrying degree, with death of around 18 million people every year from cardiovascular disease, for which diabetes and hypertension are major predisposing factors. Two major concerns are that much of this increase in Diabetes is predicated to be happened in developing countries, with a growing incidence of Type 2 Diabetes (T2D) at a younger age including some obese children even before puberty. However, in developed countries most people with diabetes are above the age of retirement. As such, understanding the aetiology of T2D is vital. It has been thought that T2D is resulting from the convergence of genetics, environment, diet and lifestyle risk factors; however, genetic susceptibility has been established as a key component of risk. Genome-wide association studies (GWAS) is a study design and analytic tool specifically developed for investigating the genetic architecture of human disease. The ultimate aim of GWAS is to identify the genetic risk factors for common complex diseases such as T2D. Traditional parametric statistical approaches such as linear modelling framework (e.g. logistic regression) have limited power for modelling the complexity of genotype-phenotype relationship that is characterized by non-linear interactions. These nonlinear interactions are necessary in discovering the aetiology of complex diseases. More specifically, the linear modelling model has some limitations such as examining each single nucleotide polymorphisms independently for the association to the phenotype ignoring the epistatic (gene-gene interactions) and non-genetics factors. This paper presents a novel approch based on the use of backpropogation technique inspired by image compression algorithm. The proposed classifier is fine-tuned for binary classification to predict those who could suffer from the disease among those who do not. Simulation results indicated that the proposed technique showed an area under the curve, true positive rate, true negative rate values of 0.92, 0.9 and 0.8 respectively when using 2500 hidden neurons.

Low complexity image compression algorithm based on hybrid DPCM for wireless capsule endoscopy

Abstract A lossless image compression algorithm with low computational complexity and to provide high image quality in limited bandwidth transmission is proposed in the paper. New algorithm is based on a new YEN colour transform for WCE images, improved and modified DPCM technique known as hybrid DPCM and new signed Golomb code with less bits skip code. The proposed compression algorithm provides better and efficient computational simplicity and improves compression ratio by 2.3% as compared to traditionally used DPCM based compression algorithms.

Research on image selective encryption and compression algorithm under hyperchaotic system

Research on image selective encryption and compression algorithm under hyperchaotic system

Multi-frame Image Processing Based on Wireless Sensor Networks

With multi-frame image processing based on wireless sensor network as research object, this study analyzes the problems existing in the two classic multi-node collaborative distributed image processing algorithms based on wavelet transform using literature analysis, simulation experiment, data analysis and other research methods, proposes a distributed image compression algorithm based on SHIHT with the application of wireless sensor network, SPIHT algorithm and wavelet transform to image compression as theoretical foundations, and adopts the matrix laboratory to simulate the energy performance of the algorithm. Through comparative analysis, the feasibility of distributed image compression algorithm based on SHIHT presented in this study is verified.