Image Compression Algorithm Research Articles

GECOM: GREEN COMMUNICATION CONCEPTS FOR ENERGY EFFICIENCY IN WIRELESS MULTIMEDIA SENSOR NETWORK

Wireless multimedia sensor network (WMSN) is one of broad wide application for developing a smart city. Each node in the WMSN has some primary components: sensor, microcontroller, wireless radio, and battery. The components of WMSN are used for sensing, computing, communicating between nodes, and flexibility of placement. However, the WMSN technology has some weakness, i.e. enormous power consumption when sending a media with a large size such as image, audio, and video files. Research had been conducted to reduce power consumption, such as file compression or power consumption management, in the process of sending data. We propose Green Communication (GeCom), which combines power control management and file compression methods to reduce the energy consumption. The power control management method controls data transmission. If the current data has high similarity with the previous one, then the data will not be sent. The compression method compresses massive data such as images before sending the data. We used the low energy image compression algorithm algorithm to compress the data for its ability to maintain the quality of images while producing a significant compression ratio. This method successfully reduced energy usage by 2% to 17% for each data.

A 13.3 Gbps 9/7M Discrete Wavelet Transform for CCSDS 122.0-B-1 Image Data Compression on a Space-Grade SRAM FPGA

Remote sensing is recognized as a cornerstone monitoring technology. The latest high-resolution and high-speed spaceborne imagers provide an explosive growth in data volume and instrument data rates in the range of several Gbps. This competes with the limited on-board storage resources and downlink bandwidth, making image data compression a mission-critical on-board processing task. The Consultative Committee for Space Data Systems (CCSDS) Image Data Compression (IDC) standard CCSDS-122.0-B-1 is a transform-based 2D image compression algorithm designed specifically for use on-board a space platform. In this paper, we introduce a high-performance architecture for a key-part of the CCSDS-IDC algorithm, the 9/7M Integer Discrete Wavelet Transform (DWT). The proposed parallel architecture achieves 2 samples/cycle while the very deep pipeline enables very high clock frequencies. Moreover, it exploits elastic pipeline principles to provide modularity, latency insensitivity and distributed control. The implementation of the proposed architecture on a Xilinx Kintex Ultrascale XQRKU060 space-grade SRAM FPGA achieves state-of-the-art throughput performance of 831 MSamples/s (13.3 Gbps @ 16bpp) allowing seamless integration with next-generation high-speed imagers and on-board data handling networking technology. To the best of our knowledge, this is the fastest implementation of the 9/7M Integer DWT on a space-grade FPGA, outperforming previous implementations.

Fractal Image Compression Using Block Indexing Technique: A Review

Fractal image compression depends on representing an image using affine transformations. The main concern for researches in the discipline of fractal image compression (FIC) algorithm is to decrease encoding time needed to compress image data. The basic technique is that each portion of the image is similar to other portions of the same image. In this process, there are many models that were developed. The presence of fractals was initially noticed and handled using Iterated Function System (IFS); that is used for encoding images. In this paper, a review of fractal image compression is discussed with its variants along with other techniques. A summarized review of contributions is achieved to determine the fulfillment of fractal image compression, specifically for the block indexing methods based on the moment descriptor. Block indexing method depends on classifying the domain and range blocks using moments to generate an invariant descriptor that reduces the long encoding time. A comparison is performed between the blocked indexing technology and other fractal image techniques to determine the importance of block indexing in saving encoding time and achieving better compression ratio while maintaining image quality on Lena image.

JPEG Lifting Algorithm Based on Adaptive Block Compressed Sensing

This paper proposes a JPEG lifting algorithm based on adaptive block compressed sensing (ABCS), which solves the fusion between the ABCS algorithm for 1-dimension vector data processing and the JPEG compression algorithm for 2-dimension image data processing and improves the compression rate of the same quality image in comparison with the existing JPEG-like image compression algorithms. Specifically, mean information entropy and multifeature saliency indexes are used to provide a basis for adaptive blocking and observing, respectively, joint model and curve fitting are adopted for bit rate control, and a noise analysis model is introduced to improve the antinoise capability of the current JPEG decoding algorithm. Experimental results show that the proposed method has good performance of fidelity and antinoise, especially at a medium compression ratio.

An optimal bit-rate allocation algorithm to improve transmission efficiency of images in deep space exploration

As fixed compression ratio is used in traditional deep space exploration image transmission application, the same compression code rate is allocated to each image. However, since the information of each image in a space observation mission is nonuniform, the image with more information will inevitably lead to more compression distortion than the image with less information. Obviously, it's not an efficient way to transmit information in terms of data importance or overall distortion. Therefore, we proposed a combinatorial optimal bit rate allocation algorithm to improve the efficiency of image transmission in space application. Different from traditional method, the rate-distortion model of wavelet coefficients for each image in a transmission task was built, and under the overall maximum transmission rate constraint, an bit-rate optimal allocation was applied for each image to minimize the overall distortion of a batch of images. The proposed algorithm can be widely used in image compression algorithm with embedded code stream characteristics, such as JPEG2000 and SPIHT. Experimental results shows that in the range of 2.6 to 10 compression ratio, the algorithm can reduce image distortion MSB by 40%∼64% at the same overall transmission code rate, which equivalent to improvement of PSNR 3 dB to 5.4 dB.

Visual Perception Adaptive Image Processing for HD Microdisplay

With the development of 5G networks, a new wave of development is ushered in the field of virtual reality (VR). The small physical size, lightweight, low power consumption, and high brightness make microdisplays the core components of VR. In order to improve the user's immersive experience and reduce the screen window effect, microdisplays are required to have higher pixel density and faster refresh speeds. However, this demand results in a large amount of data transmission and poor real‐time rendering of image information. When the human eye observes an image, the gaze point changes due to the interest of the viewer. To solve this problem, we propose an image compression algorithm based on visual perception adaptation to reduce data transmission bandwidth. The experimental results show that when sight distance is 2, the maximum comprehensive compression ratio is 42.834, the minimum comprehensive compression ratio is 11.415, the minimum clock frequency is 17.033MHz, and the maximum clock frequency is 63.916MHz. The algorithm effectively reduces the data transmission bandwidth and meets the real‐time rendering of VR application, and the rendering effect is good.

A New Image Compression by Gradient Haar Wavelet

With the development of human communications, the usage of visual communications has also increased. The advancement of image compression methods is one of the main reasons for the enhancement. This paper first presents main modes of image compression methods such as JPEG and JPEG2000 without mathematical details. Also, the paper describes gradient Haar wavelet transforms in order to construct a priliminary image compression algorithm so that sub images inherit the same amount of original image information. Then, a new image compression method is proposed based on the preliminary image compression algorithm that can improve standards of image compression. The new method is compared with original modes of JPEG and JPEG2000 (based on Haar wavelet) by image quality measures such as MAE, PSNAR, and SSIM. The image quality and statistical results confirm that can boost image compression standards. It is suggested that the new method is used in a part or all of an image compression standard.

Four-Dimensional Cone-Beam Computed Tomography Image Compression Using Video Encoder for Radiotherapy.

Four dimensional cone-beam computed tomography (4D-CBCT) images were widely used for patient positing and target localization in radiotherapy. As consisting of multiple CBCT sets, it needs more time and space for data transferring and storage. In this study the feasibility of applying video coding algorithms for 4D-CBCT image compression was investigated. Prior to compression 4D-CBCT images were arranged in an order based on breathing phase or slice location for input sequence of video encoder. Median filtering was applied to suppress noise and artifact of 4D-CBCT for improved image quality. Three popular video coding algorithms (Motion JPEG 2000, Motion JPEG AVI, and MPEG-4) were tested and their performances were evaluated on a publicly available 4D-CBCT database. The average compression ratio of MPEG-4 was 135, while the values of Motion JPEG AVI and Motion JPEG 2000 were 16 and 7, respectively. The compression rate of two ordering methods was comparable and the location-based ordering method was slightly higher. With pre-processing of median filtering, the inter-frame similarity of input sequence was improved and the resulting compression rate was increased. MPEG-4 provided extremely higher compression rate for 4D-CBCT images. The ordering method based on slice location resulted in higher compression rate than the ordering method based on breathing phase. The median filtering was effective in improving inter-frame similarity and resulted in higher compression rate. The video coding algorithms are not only applicable for 4D image modalities but also feasible for serial 3D image modalities.

Implementation of grey image compression algorithm based on variation partial differential equation

Compression is the key technology for the rapid development of multimedia technology, and images are an important part of multimedia information. For image compression, a grey image compression algorithm based on variation partial differential equations is proposed. On the encoding side, this article first uses a quad tree to segment the image, and then encodes and transmits some pixels. Secondly, an image interpolation algorithm based on variation partial differential equations is used at the decoding end to regenerate the image, effectively eliminating the block effect in the decoded image. Experiments show that this method can obtain higher compression ratio and peak signal-to-noise ratio, especially for images with larger size and less texture detail, and can better maintain large changes in grayscale in the original image. Details, which can easily remove the block effect, have high practical value.

A review on region of interest-based hybrid medical image compression algorithms

Digital medical images have become a vital resource that supports decision-making and treatment procedures in healthcare facilities. The medical image consumes large sizes of memory, and the size keeps on growth due to the trend of medical image technology. The technology of telemedicine encourages the medical practitioner to share the medical image to support knowledge sharing to diagnose and analyse the image. The healthcare system needs to ensure distributes the medical image accurately with zero loss of information, fast and secure. Image compression is beneficial in ensuring that achieve the goal of sharing this data. The region of interest-based hybrid medical compression algorithm plays the parts to reduce the image size and shorten the time of medical image compression process. Various studies have enhanced by combining numerous techniques to get an ideal result. This paper reviews the previous works conducted on a region of interest-based hybrid medical image compression algorithms.

Fast fractal image compression algorithm using specific update search

The fractal image compression (FIC) algorithm is difficult to be widely used in real-time applications due to the huge consumption of its encoding time. Inspired by the fact that in the decoding process, for any original image, a fixed point is generated by the iterations of the fractal codes, the authors propose a specific update search FIC (SUSFIC) algorithm, which uses a scale number to control the update times of the fractal codes and to find acceptable matching domain blocks rather than the best ones in the selected domain blocks pool. To further reduce the computation time, in their proposed algorithm, the image blocks created by the equidistant sampling in the range of the original blocks are used to replace themselves when calculating the correlation coefficients as the distances between the adjacent domain blocks. The experimental results presented show that their proposed SUSFIC algorithm has a significant improvement in encoding time under the premise of setting an appropriate search update threshold and maintaining image quality when compared with the state-of-the-art FIC algorithms. Therefore, it is a better FIC algorithm.

Low cost image compression algorithm with colour reproduction algorithm for transmitting video frame of capsule endoscopy using low power

Presented a low cost technique for transmitting data through radio frequency antenna using low power, which is the combination of low computational complexity image compression algorithm and simple colour reproduction algorithm. In wireless capsule endoscopy, radio frequency antenna consume more than 60% of power for transmitting data and transmitting all the RGB components consume more power. In the proposed scheme, the first compressed colour frame is transmitted followed by few number of compressed grey frames. Compression algorithm is made of simplified YUV colour space, differential pulse code modulation and Golomb Rice code. At decoder dictionary based colour reproduction algorithm is used to colorize the grey scale image using colour information of original colour image. The proposed scheme lead to saving of more than 50% power of the battery, which increases the battery life from 10 h to 18 h. This extra battery life can be used for adding extra features to capsule, which help in development of diagnosing and treatment capsules.

A New Image Compression Algorithm Based on Non-Uniform Partition and U-System

JPEG lossy image compression is a still image compression algorithm model that is currently widely used in major network media. However, it is unsatisfactory in the quality of compressed images at low bit rates. The objective of this paper is to improve the quality of compressed images and suppress blocking artifacts by improving the JPEG image compression model at low bit rates. First, the image texture adaptive non-uniform rectangular partition (ITANRP) algorithm is proposed which partitions the image into <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$8\times 8$</tex-math></inline-formula> size image blocks with high texture complexity and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$16\times 16$</tex-math></inline-formula> size image blocks with low texture complexity. Then, a new transform coding based on the complete orthogonal U-system and all-phase digital filter (APDF) is proposed for coding image blocks with different sizes. Next, a flexible adaptive quantization scheme is designed to quantize image blocks with different sizes by considering the sensitivity of the human visual system (HVS) to different texture complexities. Finally, combining the above method with the JPEG model, a novel image compression algorithm model with low algorithm complexity is proposed to solve the problem in JPEG. The experimental results demonstrate that the performance of our algorithm model outperforms the JPEG image compression algorithms, the quality of the compressed image is greatly improved, and the blocking artifacts are also significantly suppressed.

Application of particle swarm optimization BP neural network algorithm in image compression

In order to overcome the problems of weak global search ability, slow convergence speed and easy to fall into local minima in the process of image compression of BP neural network model, an image compression model based on particle swarm optimization algorithm and improved BP algorithm is proposed. In this model, a group of optimal approximate solutions of weights and thresholds of BP network are obtained through global search of particle swarm optimization according to objective function, and then the approximate solution is taken as the initial value of BP model, and the improved BP algorithm is used to conduct quadratic optimization training on these weights and thresholds to obtain the final image compression model. The experimental results show that with the same error accuracy, the quality of model compressed image reconstruction based on particle swarm BP neural network algorithm is significantly higher than that of BP and improved BP algorithm models.

An image encryption algorithm based on BP neural network and hyperchaotic system

To reduce the bandwidth and storage resources of image information in communication transmission, and improve the secure communication of information. In this paper, an image compression and encryption algorithm based on fractional-order memristive hyperchaotic system and BP neural network is proposed. In this algorithm, the image pixel values are compressed by BP neural network, the chaotic sequences of the fractional-order memristive hyperchaotic system are used to diffuse the pixel values. The experimental simulation results indicate that the proposed algorithm not only can effectively compress and encrypt image, but also have better security features. Therefore, this work provides theoretical guidance and experimental basis for the safe transmission and storage of image information in practical communication.

A high speed and memory efficient algorithm for perceptually-lossless volumetric medical image compression

With the advancements in modern medical imaging systems, the diagnostic image data has increased exponentially. The future medical applications seek medical imaging devices to be portable. Image quality and real-time processing are of prime importance in medical image compression. Therefore the volumetric medical images need to be compressed in perceptually lossless manner. Also the time taken to compress the images before transmitting (or storing), should be small. In this paper, an algorithm for lossless and perceptually-lossless medical image compression is proposed. The proposed algorithm uses two small lists and two small state-tables to encode an image. The compression efficiency is comparable to the state-of-the-art lossless compression techniques. Also the computational complexity and memory requirement are realistic for portable medical imaging devices. Combining all the three features, it is obvious that the proposed algorithm is a better candidate for image compression in comparison to all the state-of-art compression algorithms that we know of, for volumetric-medical imaging systems.

Image quality assessment by an efficient correlation‐based metric

SummaryImage quality can be measured visually. In the human visual system, a compressed image can be judged by the human eye. Image quality may not be perceived to decline in a region with low compression. However, image quality clearly declines in a region with high compression. As image compression increases, image quality gradually transitions from visually lossless to lossy. In this study, we aim to explain this phenomenon. A few images from different datasets were selected and compressed using JJ2000 and Apollo, which are well‐known image compression algorithms. Then, error‐based and correlation‐based metrics were applied to these images. The correlation‐based metrics agree with human‐vision evaluations in experiments, but the error‐based metrics do not. Inspired by the positive result of the correlation‐based metrics, a new metric named the simple correlation factor (SCF) was proposed to explain the aforementioned phenomenon. The results of the SCF show good consistency with human‐vision results for several datasets. In addition, the computation efficiency of the SCF is better than that of the existing correlation‐based metrics.

An Extremely Pipelined FPGA Implementation of a Lossy Hyperspectral Image Compression Algorithm

Segmented and pipelined execution has been a staple of computing for the past decades. Operations over different values can be carried out at the same time speeding up computations. Hyperspectral image compression sequentially processes samples, exploiting local redundancies to generate a predictable data stream that can be compressed. In this article, we take advantage of a low complexity predictive lossy compression algorithm which can be executed over an extremely long pipeline of hundreds of stages. We can avoid most stalls and maintain throughput close to the theoretical maximum. The different steps operate over integers with simple arithmetic operations, so they are especially well-suited for our FPGA implementation. Results on a Virtex-7 show a maximum frequency of over 300 MHz for a throughput of over 290 MB/s, with a space-qualified Virtex-5 reaching 258 MHz, being five times as fast as the previous FPGA designs. This shows that a modular pipelined approach is beneficial for these kinds of compression algorithms.

Improved LZW Compression Technique using Difference Method

This work attempts to give a best approach for selecting one of the popular image compression algorithm. The proposed method is designed to find the best performance approach amongst the several compression algorithms. In this work existing lossless compression technique LZW (Lempel-Ziv-Welch) is redesigned to achieve better compression ratio. LZW compression technique works on the basis of repetition of data. In a situation where all the values are distinct or repetition of data does not present the LZW can’t work properly. To avoid this problem, difference method called difference matrix method which is actually calculate the difference between two consequence data and store it in a resultant matrix is used. In this case the matrix contains repetitive data which is more effective compared to LZW technique. Another problem of LZW is dictionary overflow, because of LZW works on ASCII character there is a limit of 256 dictionary length initially. In this work dynamic dictionary method is used without using the ASCII rather than this static method. As a result, this dictionary can contain the initial value anything in a range of -256 to 255. Here ASCII values are not used because the proposed method is applicable grayscale image, where the pixel values are between in range 0 to 255. Using these two changes the proposed improved LZW method becomes more powerful that can compress a non-repetitive set of data significantly. The proposed method is applied on many standard gray images found in the literature achieved 7% to 18% more compression the normal LZW keeping quality of the image same as existing.

A Review on Fractal Image Compression Using Optimization Techniques

Image compression is an important process that has many possible application areas. The major blemish of fractal image compression is the time consuming compared to other image compression approaches. Image compression is the most essential requirement for efficient utilization of storage space and transmission bandwidth. Image compression techniques are responsible for decreasing the size of the image and keeping the quality of the recovered image. Presently many image compression algorithms are utilized to deal with the growing number of data concerned; still, area of research is to find an alternative solution to this problem. Therefore, to overcome this obstacle uses optimization techniques to solve this problem and reduces search space to find self-similarity in the given image. This review provides a study of many these techniques like Genetic Algorithm (GA), Crowding Optimization Method (COM), Particle Swarm Optimization (PSO) and Harmony Search Algorithm (HAS) and how using them in fractal image compression.