Image Compression Algorithm Research Articles

An Efficient Pass Parallel SPIHT based Image Compression using Double Density Dual Tree Complex Wavelet Transform for WSN

For the past two decades, wavelet based image compression algorithms for Wireless Sensor Network (WSN) has gained broad attention than that of the spatial based image compression algorithms. In that, Dual Tree Complex Wavelet Transforms (DTCWT) has provided better results in terms of image quality and high compression rate. However, the selection of DTCWT based image compressions for various WSN based applications is not practically suitable, due to the major limitations of WSN such as, low bandwidth, low energy consumption and storage space. Therefore, an attempt has been made in this paper to develop image compression through simulation by considering the modified block based pass parallel Set Partitioning In Hierarchical Trees (SPIHT) with Double Density Dual Tree Complex Wavelet Transform (DDDTCWT) for compressing the WSN based images. In addition, bivariate shrink method is also adopted with the DDDTCWT to obtain better image quality within less computation time. It is observed through simulation results that above mentioned proposed technique provides better performance than that of existing compression technique

Efficient Rationalization of Triplet Halfband Filter Banks and its Application to Image Compression

This paper presents a novel approach for rationalization of irrational coefficients of filters designed using three-step lifting scheme. The existing three-step lifting schemes result in the irrational filter coefficients which require infinite precision for implementation. In this work, Euler-Frobenius halfband polynomials are designed to obtain the required kernels of three-step lifting structure. Next, the efficient rationalization of the designed filter banks (FBs) is proposed to reduce the arithmetic complexity. The proposed rational FBs preserve perfect reconstruction, near-orthogonality and regularity properties of wavelet FBs. These filters with rational coefficients are then used in image compression algorithms to compress the test images in well-known Classic, EPFL, RAISE, FiveK datasets and chromosome image datasets. The performance of the proposed rational FBs is compared with the existing rational FBs. It is observed that the performance of the proposed rational FBs is improved in terms of computational cost, encoding-decoding time and compression ratio as compared to existing rational FBs.

Supervised‐learning‐based algorithm for color image compression

Supervised‐learning‐based algorithm for color image compression

Lossless Image Compression Techniques: A State-of-the-Art Survey

Modern daily life activities result in a huge amount of data, which creates a big challenge for storing and communicating them. As an example, hospitals produce a huge amount of data on a daily basis, which makes a big challenge to store it in a limited storage or to communicate them through the restricted bandwidth over the Internet. Therefore, there is an increasing demand for more research in data compression and communication theory to deal with such challenges. Such research responds to the requirements of data transmission at high speed over networks. In this paper, we focus on deep analysis of the most common techniques in image compression. We present a detailed analysis of run-length, entropy and dictionary based lossless image compression algorithms with a common numeric example for a clear comparison. Following that, the state-of-the-art techniques are discussed based on some bench-marked images. Finally, we use standard metrics such as average code length (ACL), compression ratio (CR), pick signal-to-noise ratio (PSNR), efficiency, encoding time (ET) and decoding time (DT) in order to measure the performance of the state-of-the-art techniques.

Correction for color artifacts using the RGB intersection and the weighted bilinear interpolation.

Due to the defects of optical systems, image sensors, and imperfect algorithms for image acquisition, compression, and restoration, color artifacts often appear in images obtained by imaging devices such as digital cameras and scanners. Moreover, color artifacts are difficult to eliminate because of technical limitations, even in some mature commercial cameras. On the basis of red, green, and blue (RGB) intersection (RGBI), a correction method for color artifacts is proposed in this paper, where the RGB intersection-based method can effectively detect various types of color artifacts. Also, by combining the object information with weighted bilinear interpolation, the continuity of the image is kept while restoring the real color. Experiments demonstrate that the RGBI method, which is applicable to all color images, can eliminate various types of color artifacts with accurate detection and less artifact residue, even if the image has severe color distortion or the areas of the color artifacts are small and discrete.

A novel hardware-efficient spatial orientation tree-based image compression algorithm and its field programmable gate array implementation

Set partitioning in hierarchical trees (SPIHT) has become a popular research topic for more than a decade now. This is because it is simple, besides having compression efficiency close to the state-of-the-art JPEG2000 standard. The main drawback of SPIHT is that it uses three lists to store addresses of coefficients during its operation. These lists are dynamic and in worst cases need to store more number of addresses than total coefficients. In this work, a novel hardware-efficient spatial orientation tree-based algorithm is proposed and its hardware implementation is carried out. The wavelet transformed image is partitioned into 2 $\times$ 2 blocks. Each node of spatial orientation tree (SOT) represent a block of coefficients, rather than a single coefficient. Two small state-tables are used in this algorithm to store the status of each block. In addition to this, two extremely small lists are used to store the node addresses of a single SOT tree. To store the state-tables and lists for 512 $\times$ 512 image, only 0.88 % of the memory needed by SPIHT is required for five levels of dyadic decomposition. The peak-signal-to-noise ratio (PSNR) gain of 0.1 dB to 0.3 dB at low bit rates (below 1 bpp) and 0.6 dB to 1.2 dB at high bit rates (above 1.75 bpp) in comparison to SPIHT is observed for test image Lena. A field programmable gate array (FPGA) implementation targeted for Xilinx Zynq Z-7020 is presented in the paper. The proposed architecture saves 90 % of the FPGA area used by SPIHT. The hardware efficiency of the proposed architecture is better in comparison to different existing architectures.

SVD‐based image compression, encryption, and identity authentication algorithm on cloud

Based on singular value decomposition (SVD), an image compression, encryption, and identity authentication scheme is proposed here. This scheme can not only encrypt image data which would store in the cloud but also implement identity authentication. The authors use the SVD to decompose the image data into three parts: the left singular value matrix, the right singular value matrix, and the singular value matrix. The left singular value matrix and right singular value matrix are not as important as the singular value matrix. They propose a logistic-tent-sine chaotic system to encrypt them. In this scheme, they proposed a novel authentication value calculation algorithm, which can calculate the authentication value according to related data. According to the authentication value calculated from the ciphertext, the algorithm has the perfect authentication performance, so as in the scenarios if the image is cropped or added noisy. Theoretical analysis and empirical evaluations show that the proposed system can achieve better compression performance, satisfactory security performance, and low computational complexity.

Comparison of Image Compression Methods for Image Transmission Over Wireless Sensor Network

Unlike conventional networks, in Wireless Sensor Network the nodes have constrained energy, memory and processing capabilities. These nodes deployed in a constrained environment monitor any changes in surrounding environment and transfer the changes to the cluster heads. Each node has its own memory, battery, and transceivers. Efficient utilization of these resources can result in the enhancement of network lifetime. In order to securely transfer the data in the form of images, an efficient and cost effective image compression algorithm is required. Hence, in this paper, a detailed review of image compression algorithms has been carried out. The selected algorithms are compared in terms of various performance metrics such as compression ratio, compression time, speed, type of data, etc. The results showed that algorithm proposed by Borici and Arber is the best in case of compression ratio, as it provides better compression ratio in comparison to other algorithms.

Effects of Compression Algorithms and Identification of Cancer cell using CT Coronel View Lung Image

Modern radiology techniques provide crucial medical information for radiologists to diagnose diseases and determine appropriate treatments. Hence dealing with medical image compression needs to compromise on good perceptual quality (i.e. diagnostically lossless) and high compression rate. The objective also includes finding out an optimum algorithm for medical image compression algorithm. The objective is also focused towards the selection of the developed image compression algorithm, which do not change the characterization behavior of the image.

Modified Firefly Algorithm for Vector Quantization Codebook Design in Image Compression

In the recent days, the importance of image compression techniques is exponentially increased due to the generation of massive amount of data which needs to be stored or transmitted. Numerous approaches have been presented for effective image compression by the principle of representing images in its compact form through the avoidance of unnecessary pixels. Vector quantization (VA) is an effective method in image compression and the construction of quantization table is an important process is an important task. The compression performance and the quality of reconstructed data are based on the quantization table, which is actually a matrix of 64 integers. The quantization table selection is a complex combinatorial problem which can be resolved by the evolutionary algorithms (EA). Presently, EA became famous to resolve the real world problems in a reasonable amount of time. This chapter introduces Firefly (FF) with Teaching and learning based optimization (TLBO) algorithm termed as FF-TLBO algorithm for the selection of quantization table and introduces Firefly with Tumbling algorithm termed as FF-Tumbling algorithm for the selection of search space. As the FF algorithm faces a problem when brighter FFs are insignificant, the TLBO algorithm is integrated to it to resolve the problem and Tumbling efficiently train the algorithm to explore all direction in the solution space. This algorithm determines the best fit value for every bock as local best and best fitness value for the entire image is considered as global best. When these values are found by FF algorithm, compression process takes place by efficient image compression algorithm like Run Length Encoding and Huffman coding. The proposed FF-TLBO and FF-Tumbling algorithm is evaluated by comparing its results with existing FF algorithm using a same set of benchmark images in terms of Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Signal to Noise Ratio (SNR). The obtained results ensure the superior performance of FF-TLBO and FF-Tumbling algorithm over FF algorithm and make it highly useful for real time applications.

An efficient visually meaningful image compression and encryption scheme based on compressive sensing and dynamic LSB embedding

In this paper, an efficient visually meaningful image compression and encryption (VMICE) scheme is proposed by combining compressive sensing (CS) and Least Significant Bit (LSB) embedding. First, the original image (Iorig) is compressed and encrypted into a secret image (Isec) by CS and Zigzag confusion. Next, dynamic LSB embedding is utilized to randomly embed Isec into a separate carrier image (Icar) to create the final visually meaningful (VM) cipher image (Iciph), which is the same size as Iorig. To generate the measurement matrix for CS and determine the embedding position of Isec in Icar, a 3-D Cat map is employed. The utilization of this less-chaotic system makes our algorithm easy to implement. Moreover, the initial values of Cat map are computed using Iorig, making our image compression and encryption (ICE) algorithm robust to known-plaintext and chosen-plaintext attacks. Additionally, the adoption of LSB embedding allows for Icar to be selected independently of Iorig. Simulation results and performance analyses are presented to illustrate the effectiveness and efficiency of the proposed cryptosystem.

CUDA implementation of fractal image compression

Fractal coding is a lossy image compression technique, which encodes the image in a way that would require less storage space using the self-similar nature of the image. The main drawback of fractal compression is the high encoding time. This is due to the hard task of finding all fractals during the partition step and the search for the best match of fractals. Lately, GPUs (Graphical Processing Unit) have been exploited to implement fractal image compression algorithms due to their high computational power. The prime aim of this paper is to design and implement a parallel version of the Fisher classification scheme using CUDA to exploit the computational power available in the GPUs. Fisher classification scheme is used to reduce the encoding time of fractal images by limiting the search for the best match of fractals. Encoding time, compression ratio and peak signal-to-noise ratio was used as metrics to assess the correctness and the performance of the developed algorithm. Eight images with different sizes (512 × 512, 1024 × 1024 and 2048 × 2048) have been used for the experiments. The conducted experiments showed that a speedup of 6.4 × was achieved in some images using NVIDIA GeForce GT 660 M GPU.

Application of K-means Algorithm in Image Compression

In machine learning problems, there are two main algorithms: supervised learning and unsupervised learning. Supervised learning algorithms can be used to classify data for tagged data; non-supervised learning algorithms can be used to cluster data for unlabeled data. This paper discusses the basic principles of clustering algorithm and selection of key parameters of clustering algorithm. The application of clustering algorithm in image compression is also analyzed. This paper also emphasizes the problems that should be paid attention to when using clustering. Finally, a practical case of image compression with K-means is given.

Stereo Images Encryption by OSA & RSA Algorithms

The proposed new partial encryption schemes use a secure encryption algorithm to encrypt only part of the compressed data. After application of image compression algorithm the partial encryption will applied. For two pairs of different gray scale images with the size (256 ´ 256) pixels, only 0.0244%-25% of the original data is encrypted. As a result, we see a significant reduction of time in the stage of encryption and decryption.In the compression step, the Orthogonal Search Algorithm (OSA) for motion estimation (the different between stereo images) is used. The resulting disparity vector and the remaining image were compressed by Discrete Cosine Transform (DCT), Quantization and arithmetic encoding. The image compressed was encrypted by RSA algorithm. The decoded images then compared with the original images.Good results showed in the experimental results of Peak Signal-to-Noise Ratio (PSNR), Compression Ratio (CR) and processing time. The proposed schemes of partial encryption are fast, secure and not be reducing in compression performance of the selected compression methods.

Memorability‐based image compression

This study is concerned with achieving the image compression using the concept of memorability. The authors have used memorability of an image, as a perceptual measure while image coding. In the proposed approach, a region-of-interest-based memorability preserving image compression algorithm which is accomplished via two sub-processes namely, memorability prediction and image compression is introduced. The memorability of images is predicted using convolutional neural network and restricted Boltzmann machine features. Based on these features, the memorability score of individual patches in an image is calculated and these scores are used to generate the memorability map. These memorability map values are used for optimised image compression. In order to validate the results, an eye tracking experiment with human participants is performed. The comparative analysis shows that the memorability-based compression outperforms the state-of-the-art compression techniques.

Image Compression and Encryption Algorithm Based on Hyper-chaotic Map

In this paper, aiming at defects which are low security properties, high costs of storage and transmission for exiting image encryption and compression algorithms. An algorithm which combined image compression and encryption based on hyper-chaotic map is proposed. In this algorithm, the original image is compressed by compression sensing (CS), and then the compressed image is encrypted through improved Arnold matrix transformation algorithm, Modular operation algorithm and combined the 3D hyper-chaotic map. The experimental results and theoretical analyses show that the proposed algorithm has superior safety performance and compression characteristics, which may reduce the costs of data transmission and improve the encryption efficiency. What’s more, it provides the theoretical guidance and experimental basis for digital image encryption in practical application.

Lightweight Server-Assisted H-K Compression for Image-Based Embedded Wireless Sensor Network

Embedded wireless imaging systems based on wireless camera sensor network can be used to observe biological phenomena unobtrusively in various environmental monitoring applications. In our prior work, we have shown the feasibility and benefits of such a system through actual deployments. We have also shown that low-complexity data compression schemes can be employed to improve image transfer rate or to lower the energy costs of communication. In this paper, we extend upon our prior work and propose a scheme called as H–K compression , a simple lightweight image compression algorithm combining the ideas of Huffman coding and K-means clustering. Specifically, H–K compression applies K-means clustering for pixel color grouping and Huffman coding for the group color encoding, but combines the two schemes into one algorithm which can reduce the computational cost. Using 100 000 images collected from our pilot deployments at James Reserve, we study the applicability and impact of the proposed algorithm. Our results suggest that the cost of running the learning steps on an embedded sensor node may outweigh the benefit of compression, but offloading the learning can provide significant energy gains. Evaluations using the data set show that our proposed scheme compresses the data by $\sim$ 57% and reduces power usage by $\sim$ 43% when sending image updates from a bird nest periodically every 15 min.

Research on adaptive fractal image compression algorithm based on flexible classification technology

This paper proposes a flexible classification technique for accelerating image matching and improving coding quality, using a fast adaptive fractal image compression method with four fork tree as image division and flexible classification technology, which is a kind of pre-segmentation of images. With using the self-similarity of each part of the image, the fractal encoding is carried out. In order to reduce the encoding time to dozens of seconds, it eliminate the block effect and realize the fast fractal coding of color image, so as to speed up the encoding speed and further improve the image comprehensive performance of compression ratio and signal-to-noise ratio/encoding time.

Research on heterogeneous distributed fractal image compression algorithm based on parallel genetic algorithm and iterative function

This paper briefly introduces the method and implementation of image compression of heterogeneous distributed genetic and iterative function system (IFS). Based on the traditional parallel genetic algorithm, a heterogeneous distributed model of parallel fractal image compression based on genetic algorithm is implemented, and the corresponding complex model design scheme is put forward. The results show that the algorithm has better search ability, its comprehensive performance such as signal-to-noise ratio/compression time is further improved, the decoding image is similar to the original image, and has high image quality effect.

An Improved Image Compression Technique Using EZW and SPHIT Algorithms

Uncompressed form of the digital images are needed a very large storage capacity amount, as a consequence requires large communication bandwidth for data transmission over the network. Image compression techniques not only minimize the image storage space but also preserve the quality of image. This paper reveal image compression technique which uses distinct image coding scheme based on wavelet transform that combined effective types of compression algorithms for further compression. EZW and SPIHT algorithms are types of significant compression techniques that obtainable for lossy image compression algorithms. The EZW coding is a worthwhile and simple efficient algorithm. SPIHT is an most powerful technique that utilize for image compression depend on the concept of coding set of wavelet coefficients as zero trees. The proposed compression algorithm that combined dual image compression techniques (DICT) invest an excellent features from each methods, which then produce promising technique for still image compression through minimize bits number that required to represent the input image, to the degree allowed without significant impact on quality of reconstructed image.
 The experimental results present that DICT will improve the image compression efficiency between 8 to 24%, and will result high performance of metrics values.