Image Compression Algorithm Research Articles

Coding algorithm for grayscale images based on Linear Prediction and dual mode quantization

This paper proposes a novel algorithm for grayscale image compression based on dual mode quantization that is supported by improved linear prediction scheme. The idea of dual mode quantization comes from desire to exploit advantages of the both uniform and piecewise uniform quantizers, designed for discrete input samples. The algorithm performs quantizers with a low and medium number of quantization levels and with a fixed codeword length by using a pixel value prediction in preprocessing. The correlation of adjacent pixels is exploited as the main idea for improving the quality of image compression. The proposed prediction is linear and very simple for practical realization. An analysis of reconstructed image quality is presented considering several parameters and by comparing with few other methods – BTC, DPCM and with methods that use transformation coding. Experiments are done applying the proposed compression model to several standard grayscale test images. Special attention is given to determination of thresholds values that determine whether and which of the two offered quantizers to use. Moreover, method for determining the value of proposed quantizer’s variance is explained. Obtained results show that proposed model ensures gain up to 6.14 [dB] compared to the BTC model that uses fixed piecewise uniform quantization for discrete input without a pixel value prediction as well as gain up to 5.89 [dB] compared to the DPCM model that applies dual predictor. The proposed algorithm could find application in current grayscale image compression and video standards.

Analisa Pengiriman Citra Terkompresi SPIHT dengan Teknik Spread Spectrum Direct Sequence (DS-SS)

Pada era teknologi informasi saat ini permintaan akan kebutuhan komunikasi multimedia terus meningkat. Situs web di internet dibuat semenarik mungkin dengan menyertakan visualisasi berupa gambar atau video yang dapat diputar. Citra (image) sebagai salah satu komponen multimedia memegang satu peranan sangat penting sebagai bentuk informasi visual. File citra memiliki kapasitas yang cukup besar, dan untuk mengaksesnya dibutuhkan waktu yang lama, sehingga dibutuhkan sebuah teknik kompresi data, dengan tujuan untuk mengurangi redudansi dari data-data yang terdapat dalam file citra, sehingga file citra dapat disimpan atau ditransmisikan secara efisien. Dalam hal ini file citra akan dikompresi dengan menggunakan algoritma SPIHT (Set Partitioning In Hierarchical Trees), SPIHT merupakan algoritma kompresi citra yang mampu mencapai rasio kompresi yang tinggi. Dengan cara mengkodekan koefisien hasil transformasi wavelet secara bertahap, dikarenakan aliran data terkompresi sangat rentan terhadap gangguan kanal. Pada proses transmisi digunakan teknik Direct Sequence Spread Spektrum (DS-SS). Hasil yang didapat setelah simulasi, berupa citra-citra rekonstruksi yang diukur dengan parameter penilaian obyektif yang berdasarkan nilai Peak Signal to Noise Ratio. Penilaian obyektif kualitas citra rekonstruksi yang efisien dengan rate 2bpp diperoleh nilai PSNR 46.834 dB.

OpenCL-based optimization methods for utilizing forward DCT and quantization of image compression on a heterogeneous platform

Recent computer systems and handheld devices are equipped with high computing capability, such as general purpose GPUs (GPGPU) and multi-core CPUs. Utilizing such resources for computation has become a general trend, making their availability an important issue for the real-time aspect. Discrete cosine transform (DCT) and quantization are two major operations in image compression standards that require complex computations. In this paper, we develop an efficient parallel implementation of the forward DCT and quantization algorithms for JPEG image compression using Open Computing Language (OpenCL). This OpenCL-based parallel implementation utilizes a multi-core CPU and a GPGPU to perform DCT and quantization computations. We demonstrate the capability of this design via two proposed working scenarios. The proposed approach also applies certain optimization techniques to improve the kernel execution time and data movements. We developed an optimal OpenCL kernel for a particular device using device-based optimization factors, such as thread granularity, work-items mapping, workload allocation, and vector-based memory access. We evaluated the performance in a heterogeneous environment, finding that the proposed parallel implementation was able to speed up the execution time of the DCT and quantization by factors of 7.97 and 8.65, respectively, obtained from 1024 × 1024 and 2084 × 2048 image sizes in 4:4:4 format.

Energy‐efficient low bit rate image compression in wavelet domain for wireless image sensor networks

The problem related to energy conservation in a resource-constrained wireless visual sensor network applied for habitat monitoring, border patrol, battlefield surveillance and so on, where images have to be transmitted over a wireless medium with limited bandwidth, is addressed. The proposed approximation band transform algorithm is unique and novel since it extracts and encodes only approximations of the image using fixed-point arithmetic without applying highpass analysis filters. This low bit rate image compression algorithm is specifically designed for resource-constrained low-power sensors. Its performance is analysed in terms of bit rate (bits per pixel), image quality, processing time and its energy consumption in an Atmel Atmega128 processor. It is shown that this algorithm consumes only 12% of energy needed by joint photographic experts group (JPEG) [independent JPEG group (IJG)] version and gives better results than JPEG at very low bit rates and thus the proposed scheme can significantly enhance the lifetime of low-power sensors.

A Comparative Study on Adaptive Lifting Based Scheme and Interactive Artificial Bee Colony Algorithm for Image Compression

A Comparative Study on Adaptive Lifting Based Scheme and Interactive Artificial Bee Colony Algorithm for Image Compression

A new wavelet based efficient image compression algorithm using compressive sensing

We propose a new algorithm for image compression based on compressive sensing (CS). The algorithm starts with a traditional multilevel 2-D Wavelet decomposition, which provides a compact representation of image pixels. We then introduce a new approach for rearranging the wavelet coefficients into a structured manner to formulate sparse vectors. We use a Gaussian random measurement matrix normalized with the weighted average Root Mean Squared energies of different wavelet subbands. Compressed sampling is finally performed using this normalized measurement matrix. At the decoding end, the image is reconstructed using a simple l1-minimization technique. The proposed wavelet-based CS reconstruction, with the normalized measurement matrix, results in performance increase compared to other conventional CS-based techniques. The proposed approach introduces a completely new framework for using CS in the wavelet domain. The technique was tested on different natural images. We show that the proposed technique outperforms most existing CS-based compression methods.

Design and Implementation of 2D-DCT by Using Arai Algorithm for Image Compression

Discrete Cosine Transform (DCT) exploits cosine functions; it transforms a signal from spatial representation into frequency domain. It is one of the most widely used techniques for the compression of the image. The main goal of image compression using DCT is the reduction or elimination of redundancy in data representation in order to achieve reduction in storage and communication cost. In this work, we proposed the low complexity architecture for the computation of an algebraic integer (AI) based 8-point DCT. The proposed approach is fast and provides low complexity.

A New Approach Based on Order Reduction Using Sub Image Formation in Minimizing the Computation Time for Image Compression

Image compression is the process to remove the redundant information from the image so that only essential information can be stored to reduce the storage size and transmission time. Advancement in still image compression becomes essential for various applications like medical imaging and multimedia applications. The task of image compression generally refers to deriving a near approximated compressed image for the given input image. Methods for tackling this problem have to do a delicate balancing act of suppressing the unwanted effects in visualizing without losing features of interest. Different techniques were already proposed in this area which is not sufficient to maintain good quality of the compressed image without sacrificing the compression ratio. Thus, new image compression algorithm based on order reduction using sub-image formation is proposed for lossy compression scheme. Obviously, the tradeoff between compression ratio and picture quality is an important issue in image compression. The importance of running time of compression was further investigated as this process can be used in high speed data transmission applications. The simulation process is carried out determining the computation time using MATLAB. The algorithm tested for still images of different size too.

IJIREEICE - Electrical, Electronics, Instrumentation and Control

In many practical scenarios, image encryption has to be conducted prior to image compression. This has led to the problem of how to design a pair of image encryption and compression algorithms such that compressing the encrypted images can still be efficiently performed. In this paper, we design a highly efficient image encryption-then- compression (ETC) system, where both lossless and lossy compressions are considered. The proposed image encryption scheme operated in the prediction error domain is shown to be able to provide a reasonably high level of security. We also demonstrate that an arithmetic coding-based approach can be exploited to efficiently compress the encrypted images. More notably, the proposed compression approach applied to encrypted images is only slightly worse, in terms of compression efficiency, than the state-of-the-art lossless/lossy image coders, which take original, unencrypted images as inputs. In contrast, most of the existing ETC solutions induce significant penalty on the compression efficiency.

An Image Compression Scheme Based on Fuzzy Neural Network

Image compression technology is to compress the redundancy between the pixels to reduce the transmission broadband and storage space by using the correlation of the image pixels. Fuzzy neural network effectively integrates neural network technology and fuzzy technology; combines learning, self-adaptivity, imagination and identity and uses rule-based reasoning and fuzzy information processing in the nodes; thus greatly improving the transparency of fuzzy neural network. This paper mainly investigates the applications of fuzzy neural network in image compression and realizes the image compression and reconstruction of fuzzy neural network. It is demonstrated in the simulation experiment that the image compression algorithm based on fuzzy neural network has significant advantages in training speed, compression quality and robustness.

A Novel Image Compression Algorithm Based on Multitree Dictionary and Perceptual-based Rate-Distortion Optimization

This paper presents a novel block-based image coding algorithm which applies a tree-structured multitree dictionary and a perceptual-based rate distortion optimization scheme. While multitree dictionary is employed to support a very large number of different tilings, perceptual-based rate distortion optimization utilizes the SSIM metric, instead of popular MSE metric, to allocate bit rate according to human visual system. Experimental results show that our proposed method outperforms many existing techniques in both subjective and objective image quality measures.

REVIEW ON OPTIMIZATION TECHNIQUES USED FOR IMAGE COMPRESSION

Image compression is most essential requirement for efficient utilization of storage space and transmission bandwidth. Image compression technique involves reducing the size of the image without degrading the quality of the image. Currently many image compression algorithms are used to deal with increasing amount of data involved but still finding the alternative solution is the area of research. This paper reviews some of the Meta heuristic optimization algorithms used for image compression. These algorithms are based on swarm intelligence. Swarm intelligence is a relatively new area that deals with the study of behavior among many entities or objects interacting within the natural or artificial systems. In past few years Swarm Intelligence based algorithms have been applied to a wide variety of problems in combinatorial and continuous optimization, telecommunications, swarm robotics, networking, image processing etc. This paper provides an insight of many optimization techniques used for image compression like Ant Colony Optimization (ACO) algorithm , Harmony Search Algorithm (HSA) and Artificial Bee Colony algorithm, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Ant Colony Optimization algorithm is inspired by the behavior among real ant’s while searching for the food source. Harmony Search Algorithm is inspired by the harmony improvisation process followed while playing music. Particle swarm optimization is an optimization technique inspired by social behavior of bird flocking or fish schooling. Artificial Bee Colony algorithm is motivated by the behavior exhibited by honey bees while searching for the food source. Genetic Algorithm is based on processes observed in the natural evolution.

English

Lossless image compression is a class of image compression algorithms that allows the original image to be perfectly reconstructed from the compressed image. This undertaking shows another lossless color image compression algorithm based on pixel prediction and arithmetic coding. Lossless squeezing of a Red, Green and Blue (RGB) picture is carried out by first decorrelating utilizing Reversible Color Transform (RCT). The got Y part is then compacted by a conventional lossless grayscale picture clamping strategy. The chrominance image is encoded utilizing Arithmetic coding and pixel prediction system. By utilizing RCT the forecast lapse is characterized and arithmetic coding is connected to the mistake signal. The packed image and encoded picture is joined to structure a lossless compacted RGB picture. It is demonstrated that this system diminishes the bit rates contrasted and JPEG 2000 and JPEG-XR. With a specific end goal to further lessen the bit rate the compression strategies and the pixel prediction strategy can be altered for better execution. Keywords-Lossless image compression, Pixel Prediction, Arithmetic Coding, RCT, Huffman coding

Image Compression using Super-Resolution Technique: A Review

With the rapid increasing demand of digital networks, people are likely to demand more and more accurate images which can be efficiently achieved using image compression. In image compression the irrelevant data is removed from the image keeping in mind that the data is stored in an adequate form. By doing this the data from the original image is sometimes is lost. Based on this, in the review paper the needs, its principle and various file formats of image compression are discussed. The paper also focuses on algorithms of image compression.

Efficient Quantization Method for Biometric Fingerprint Image Compression

The choice of quantization method and the requirement to achieve a trade-off between compressed image quality and degradation are very crucial in the overall performance of a lossy image compression algorithm. In this paper, uniform and non-uniform scalar quantization schemes of biometric fingerprint image were studied. Comparative analyses of non-uniform quantization methods were also conducted and these include dither-based quantization and the Lloyd-Max quantization methods. The quality of the quantized output fingerprint image was determined in terms of Signal-to-Quantization Noise Ratio (SQNR). The degree of distortion or quantization error was determined in terms of the Mean Square Quantization Error (MSQE). The non-uniform quantization method performed better than the uniform quantization method in terms of the SQNR and MSQE values. It was also found out that, the performance of dither-based non-uniform quantization on biometric fingerprint image is not as efficient as the LloydMax approach when the number of bits used in the quantization process increased. The results showed that the higher the number of bits used in the quantization process the higher the quality and the less the distortion in the resulting images.

Improvement of JPEG for Color Images by Incorporation of CAM02-UCS and Cubic Spline Interpolation

Image data is always a major fraction of the huge data to be stored or transmitted. That is why researchers have been evolved in finding out different ways and techniques to increase compression rate and reduce information loss. This research investigated the improvement of JPEG compression algorithm by incorporating cubic spline interpolation (CSI) in the sampling stage and four different color spaces in the color space transformation stage. JPEG 1992 standard was considered and results were compared with previous works done by different researchers. The sampling and color space transformation stages of the JPEG algorithm were taken into consideration. In the color space transformation stage, two linear and non-uniform color spaces RGB and YIQ, and two uniform color spaces CIELAB and the CIECAM02 based uniform color space CAM02-UCS were incorporated and investigated. The sampling stage of JPEG contributes much to improve the compression rate at the cost of loss of some information. Current study incorporated cubic spline interpolation technique to reduce the information loss at this typical stage. The CIEDE2000 color difference formula, which is best correlated with the human visual perception, was used as metric to investigate performance of newly proposed improvements in JPEG algorithm for color image compression. The test results showed that the proposed modifications in the two stages of JPEG algorithm improved its performance in terms of compressibility and quality, and the difference in performance was statistically significant. Psychophysical experiments were also performed which validated the test results.

Hybrid Prediction and Fractal Hyperspectral Image Compression

The data size of hyperspectral image is too large for storage and transmission, and it has become a bottleneck restricting its applications. So it is necessary to study a high efficiency compression method for hyperspectral image. Prediction encoding is easy to realize and has been studied widely in the hyperspectral image compression field. Fractal coding has the advantages of high compression ratio, resolution independence, and a fast decoding speed, but its application in the hyperspectral image compression field is not popular. In this paper, we propose a novel algorithm for hyperspectral image compression based on hybrid prediction and fractal. Intraband prediction is implemented to the first band and all the remaining bands are encoded by modified fractal coding algorithm. The proposed algorithm can effectively exploit the spectral correlation in hyperspectral image, since each range block is approximated by the domain block in the adjacent band, which is of the same size as the range block. Experimental results indicate that the proposed algorithm provides very promising performance at low bitrate. Compared to other algorithms, the encoding complexity is lower, the decoding quality has a great enhancement, and the PSNR can be increased by about 5 dB to 10 dB.

Novel superimposed diamond search algorithm for medical image compression

A novel search algorithm called superimposed diamond search algorithm SDSA based on lifting wavelet transform LWT for medical image compression is proposed in this paper. Fuzzy C means clustering FCM is applied to extract the region of interest ROI from the medical image. MAXSHIFT method is used to scale the coefficients so that the bits associated with the ROI are placed in higher bit planes than the bits associated with the background without the requirement of the shape information and without the need for calculating the ROI mask. SDSA keeps track of significant pixels of wavelet sub-band in hexagonal search in the scan order of left to right and top to bottom. The experimental results show the good compression ratio over the other existing methods such as set partitioning in hierarchical trees SPIHT and embedded zerotree wavelet EZW.

SPIHT: A set partitioning in hierarchical trees algorithm for image compression

SPIHT: A set partitioning in hierarchical trees algorithm for image compression

The Impact of KLT Coder on the Image Distortion in Astronomy

Presented paper is devoted to the application of Karhunen-Loève transform (KLT) for compression and to study of KLT impact on the image distortion in astronomy. This transform is an optimal fit for images with Gaussian probability density function in order to minimize the root mean square error (RMSE). The main part of the encoder is proposed in relation to statistical image properties. Selected astronomical image processing algorithms are used for the encoder testing. The astrometry and point spread function distortion are selected as the most important criteria. The results are compared with JPEG2000 standard. The KLT encoder provides better results from the RMSE point of view. These results are promising and show the novel approach to the design of lossy image compression algorithms and also suitability for algorithms of image data structuring for retrieving, transfer, and distribution.