Digital Image Compression Research Articles

IMPLEMENTASI DAN ANALISIS KOMPRESI HYBRID PADA CITRA MEDIS DIGITAL HASIL RONTGEN KANKER PAYUDARA

Digital image compression is an attempt to transform the data or symbols of composing digital images into data or other symbols without causing significant changes in the digital image to the human views. The goal is to reduce the redundancy of the data contained in the image so it can be stored or transmitted efficiently. Breast cancer is one of the greatest cancer cases in Indonesia which requires so many digital data x-rays. This research is intended to compress digital X-ray images of breast cancer with more efficient size without altering or reducing its information and it still can be assessed by human view. Using hybrid compression method, they are 2D DCT (Two Dimensional Discret Cosine Transform) and Huffman Method, Medical expert gave 3 of 4 rate when it reach 34.36% compression ratio with 40dB PSNR. This test result used 0 till 3 depth of zero in 2D DCT quantization process.

Highly efficient density-based topology optimization using DCT-based digital image compression

In this brief note we show that the number of design variables in density-based topology optimization can be phenomenally reduced using discrete cosine transform (DCT), which is one of the most frequently used transforms in digital image compression. Only quite a few nonzero DCT coefficients corresponding to low frequency components are needed to generate optimized topology with high resolution. Through two examples, one for compliance minimization and the other for heat conduction, we show that the density method can be surprisingly efficient than people have thought. Moreover, there is no need to use additional density filter or sensitivity filter since high frequency components are inherently filtered by the DCT-based compression.

Image forgery detection by semi-automatic wavelet soft-Thresholding with error level analysis

Abstract In this paper a method for detection of image forgery in lossy compressed digital images known as error level analysis (ELA) is presented and it’s noisy components are filtered with automatic wavelet soft-thresholding. With ELA, a lossy compressed image is recompressed at a known error rate and the absolute differences between these images, known as error levels, are computed. This method might be weakened if the image noise generated by the compression scheme is too intense, creating the necessity of noise filtering. Wavelet thresholding is a proven denoising technique which is capable of removing an image’s noise avoiding altering other components, like high frequencies regions, by thresholding the wavelet transform coefficients, thus not causing blurring. Despite its effectiveness, the choice of the threshold is a known issue. However there are some approaches to select it automatically. In this paper, a lowpass filter is implemented through wavelet thresholding, attenuating error level noises. An efficient method to automatically determine the threshold level is used, showing good results in threshold selection for the presented problems. This automatic threshold level can be fine tuned by a parameter k . Standard test images have been doctored to simulate image tampering, error levels for these images are computed and wavelet thresholding is performed to attenuate noise. Results are presented, confirming the method’s efficiency at noise filtering while preserving necessary error levels. The main contribution of this paper is the investigation of Daubechies wavelets with semi-automatic soft-thresholding in order to highlight forgeries in images. These results can be further extended by expert systems to classify and identify forgeries.

Comparison of Discrete Cosine Transforms (DCT), Discrete Fourier Transforms (DFT), and Discrete Wavelet Transforms (DWT) in Digital Image Watermarking

Digital Image Watermarking is used recently to secure the image by embedding another digital image. It is typically used to identify ownership of the copyright of the signal. Frequency domain transformation methods used widely in Digital Image Compression and Digital Image Watermarking. They reduce the weakness of classics digital image watermarking such as Least Significant Bit (LSB) methods which is more noise-tolerant. Popular transformation method used are Two Dimensional Discrete Cosine Transform (2D DCT), Two Dimensional Discrete Fourier Transforms (2D DFT), and Two Dimensional Discrete Wavelet Transform (2D DWT). This paper will show the comparison result of those three transformation method. The experiments are comparison analysis of image watermark quality using Peak Signal to Noise Ratio (PSNR), color converting, image resizing, image optical scanning and the noise-tolerant of the image watermarked by giving Gaussian noise.

A Fast DCT Algorithm for Watermarking in Digital Signal Processor

Discrete cosine transform (DCT) has been an international standard in Joint Photographic Experts Group (JPEG) format to reduce the blocking effect in digital image compression. This paper proposes a fast discrete cosine transform (FDCT) algorithm that utilizes the energy compactness and matrix sparseness properties in frequency domain to achieve higher computation performance. For a JPEG image of8×8block size in spatial domain, the algorithm decomposes the two-dimensional (2D) DCT into one pair of one-dimensional (1D) DCTs with transform computation in only 24 multiplications. The 2D spatial data is a linear combination of the base image obtained by the outer product of the column and row vectors of cosine functions so that inverse DCT is as efficient. Implementation of the FDCT algorithm shows that embedding a watermark image of 32 × 32 block pixel size in a 256 × 256 digital image can be completed in only 0.24 seconds and the extraction of watermark by inverse transform is within 0.21 seconds. The proposed FDCT algorithm is shown more efficient than many previous works in computation.

An algorithm for information embedding into compressed digital images based on replacement procedures with use of optimization

In the paper, we consider a particular direction of digital steganography — information embedding into the compressed JPEG images. A scheme of information embedding based on procedures of DCT-coefficients replacement is introduced. Variants of the scheme algorithmic implementation are offered and investigated. A genetic algorithm is used for the improvement of the embedding quality. The main result of the investigation is a steganographic algorithm of information embedding into the compressed JPEG images. This algorithm utilizes an unstable region of embedding at the level of one block of DCT-coefficients. The choice of an optimum region of embedding is performed by the genetic algorithm.

Erratum to: Color-secure digital image compression

Erratum to: Color-secure digital image compression

Imaging Informatics: 25 Years of Progress.

The science and applications of informatics in medical imaging have advanced dramatically in the past 25 years. This article provides a selective overview of key developments in medical imaging informatics. Advances in standards and technologies for compression and transmission of digital images have enabled Picture Archiving and Communications Systems (PACS) and teleradiology. Research in speech recognition, structured reporting, ontologies, and natural language processing has improved the ability to generate and analyze the reports of imaging procedures. Informatics has provided tools to address workflow and ergonomic issues engendered by the growing volume of medical image information. Research in computeraided detection and diagnosis of abnormalities in medical images has opened new avenues to improve patient care. The growing number of medical-imaging examinations and their large volumes of information create a natural platform for "big data" analytics, particularly when joined with high-dimensional genomic data. Radiogenomics investigates relationships between a disease's genetic and gene-expression characteristics and its imaging phenotype; this emerging field promises to help us better understand disease biology, prognosis, and treatment options. The next 25 years offer remarkable opportunities for informatics and medical imaging together to lead to further advances in both disciplines and to improve health.

Method of lossless image compression in the Earth remote sensing systems

A method of lossless digital image compression is presented in the paper. Its implementation demands minimum computing resources and it can be efficiently used in Earth remote sensing systems. The method makes possible maximum removal of statistical redundancy of images without spectral transformations. A digital image is resolved into bit planes which are processed independently of one another. That allows their parallel processing without any restriction on the pixel length of the image. Each bit plane is approximated by a two-dimensional Markov random field with discrete values. Prediction of elements of the bit plane is accomplished on the basis of the theory of conventional Markov processes. Only operations of logical comparison are used for the prediction, whereas computational operations such as multiplication and addition are not required, which makes it possible to increase the speed of image processing. The research conducted shows that the method is not inferior to known methods of lossless compression in its efficiency, while being considerably superior to them in the speed of processing. The method is applicable both to panchromatic and color images and can be used in any systems of digital image processing.

Digital image compression for a 2f multiplexing optical setup

In this work a virtual 2f multiplexing system was implemented in combination with digital image compression techniques and redundant information elimination. Depending on the image type to be multiplexed, a memory-usage saving of as much as 99% was obtained. The feasibility of the system was tested using three types of images, binary characters, QR codes, and grey level images. A multiplexing step was implemented digitally, while a demultiplexing step was implemented in a virtual 2f optical setup following real experimental parameters. To avoid cross-talk noise, each image was codified with a specially designed phase diffraction carrier that would allow the separation and relocation of the multiplexed images on the observation plane by simple light propagation. A description of the system is presented together with simulations that corroborate the method. The present work may allow future experimental implementations that will make use of all the parallel processing capabilities of optical systems.

Medical Image Compression using Adaptive Subband Threshold

Medical imaging techniques such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT) and Ultrasound (US) produce a large amount of digital medical images. Hence, compression of digital images becomes essential and is very much desired in medical applications to solve both storage and transmission problems. But at the same time, an efficient image compression scheme that reduces the size of medical images without sacrificing diagnostic information is required. This paper proposes a novel threshold-based medical image compression algorithm to reduce the size of the medical image without degradation in the diagnostic information. This algorithm discusses a novel type of thresholding to maximize Compression Ratio (CR) without sacrificing diagnostic information. The compression algorithm is designed to get image with high optimum compression efficiency and also with high fidelity, especially for Peak Signal to Noise Ratio (PSNR) greater than or equal to 36 dB. This value of PSNR is chosen because it has been suggested by previous researchers that medical images, if have PSNR from 30 dB to 50 dB, will retain diagnostic information. The compression algorithm utilizes one-level wavelet decomposition with threshold-based coefficient selection.

Noise Reduction in Compressed Images Using Improved Fuzzy Transform Technique

In the area of digital image compression, computer algorithms are used to perform processing of images and compression. It deals with developing a digital system that perform operations on digital image. It has many advantages using in digital camera, film, Satellite, X-ray and many more applications. Image compression is a technique used to save the storage space normally used to compress images and videos. Number of compression algorithms are used like run length encoding, huffman coding, discrete cosine transform, vector quantization, fuzzy transform. This gives a brief idea on improved fuzzy technique to reduce noise in compressing image. There are so many techniques for compression but in this only present the techniques of improved fuzzy method to reduce noise and compressed the image by using edge detection. The main idea behind applying this is we have to preserve the well significant edges as Jpeg is the popular standard but at low bit rate Jpeg exhibits blocking artifacts means noisy effects that affect the visual image quality so to produce high visual quality image at low bit rate ,the algorithm is efficient and simple. The proposed algorithm consists of three steps. First, image is preprocessed using competitive fuzzy edge detection. Second, based on edge information image is compressed and decompressed using improved fuzzy transform. Third, reconstructed image is post processed using hybrid median filter for artifact reduction. Analysis proves the superiority of proposed algorithm. The results of different number of coefficients are compared with the value of PSNR, MSE of algorithm. After comparison of techniques it is found to be efficient for visualization.

Parameterization of the nonlinear Greham predictor for digital image compression

Parameterization of the nonlinear Greham predictor is performed for a digital image compression method based on the differential pulse code modulation. The predictor automatically selects different methods for calculating each pixel in the image based on the availability and intensity of that pixel contour. A fast learning procedure that optimizes the prediction parameters is performed before the actual compression. In the course of optimization, the minimum sum of absolute values of prediction errors is provided. For this purpose, a recursive procedure is used, whose computational complexity is independent of the image size. The estimation of the computational complexity of the proposed predictor is conducted. To study the predictors computational experiments are carried out on real images. A gain the proposed predictor offers in terms of the root mean square error when compared with the prototypes is demonstrated. In addition, a gain that the compression method based on the differential pulse code modulation with the proposed predictor has over the JPEG compression method in terms of the maximum error is demonstrated.

Hyperspectral remote sensing data compression and protection

In this paper, we consider methods for hyperspectral image processing, required in systems of image formation, storage, and transmission and aimed at solving problems of data compression and protection. A modification of the digital image compression method based on a hierarchical grid interpolation is proposed. Methods of active (on the basis of digital watermarking) and passive (on the basis of artificial image distortion detection) data protection against unauthorized dissemination are developed and investigated.

Homotopy based on Marcus–Wyse topology and its applications

The present paper establishes a homotopy based on Marcus–Wyse (for brevity M-) topology, which can contribute to the classification of 2D digital images in an M-topological approach. Since M-topology is considered in the Euclidean 2D space with integer coordinates, it can be used in studying 2D digital images from the viewpoints of both digital topology and digital geometry such as image processing, image analysis, computer graphics, mathematical morphology and so forth. To develop the homotopy, the present paper uses two maps such as an M-continuous map and Marcus–Wyse adjacency (for brevity MA-) map because they have their own features and merits. Besides, using this homotopy, the present paper proposes an MA-homotopy equivalence and further, MA-contractibility which can be also used in classifying digital images. Finally, the paper establishes an MA-homotopic thinning derived from the above homotopy, which can contribute to the compression of 2D digital images.

Method of compression of digital halftone images on the basis of markov chains with several states

The paper presents a mathematical model (MM) of a digital halftone image (DHI) on the basis of a two-dimensional Markov chain with several states; its adequacy to real images is analyzed. On the basis of MM a method of DHI compression is developed. The method provides separation of DHI into binary images with subsequent combining of two digits in a plane. Each plane is considered as a two-dimensional random Markov process with several (N=4) states. On the basis of the theory of random Markov processes prediction of the plane elements is carried out. All incorrectly predicted elements are located in a bit stream and serve as a reference for the recovery of the image. Separation of areas containing background noise with a structure similar to the white gaussian noise (WGN) is carried out beforehand for the planes containing low- order bits, these areas are not stored, they are filled with WGN samples in case of restoration. The efficiencyof the method is no inferior to that of known methods of compression based on DCT or DWT, it does not involve computing operations and makes it possible to work with multidigital images (8 and more digits) without increase in the time of compression due to parallel processing of the planes.

Analysis of Efficiently Lossless Compression of Color Image by using Deferent Algorithms

Digital Color Image required large amount of space to store and large bandwidth to transmit it. Due to limitations in bandwidth and in storage space, it is vital requirement to compresses digital color image. Color image compression is required with negligible loss in image quality for the effective use of further restored image. To meet this, numerous image compression techniques are developed in last several years. This paper analyses these techniques and presents a compression among them to find out the best method for digital color image compression. The objective of this paper is also to find out the best approach for developing new way of digital color image compression with minimum loss in image quality.

Application of Digital Imaging for Cytopathology under Conditions of Georgia

Digital imaging in cytopathology can be defined as a series of procedures, those contributing to the quality of the displayed on the computer monitor final image. The procedures include sample preparation and staining, optical image formation by the microscope and afterwards the digital image sampling by the camera sensor; which means digital image post-processing and compression, transmission across the network and display on the monitor. A large amount of data about digital imaging exist. However, there are existing the problems with standardization and understanding of the digital imaging complete process. The field of digital imaging is rapidly evolving. The new models and protocols of the digital imaging are developed around the world, but in Georgia this field is still at evolving stages and revolves around static telecytology. It has been revealed, that the application of easy available and adaptable technology together with the improvement of infrastructure conditions is the essential basis for digital imaging. This tool is very useful for implementation of second opinion consultations on difficult cases. Digital imaging significantly increases knowledge exchange and thereby ensured a better medical service. The article aims description of digital imaging application for cytopathology under conditions of Georgia.

Метод быстрого сжатия изображений без потерь

A suggested digital image compression method is characterized by simplicity of implementation and the lack of computing operations at the stage of prediction. Background: Miniature space watch facilities (small satellites) cannot provide a continuous data transmission because of the severely constrained requirements for energy resources usage efficiency that produces the necessity of new energy efficient low-cost digital images compression methods, which would not be inferior to the known multidigit digital images compression methods with high resolution but surpass them. Methods: The algorithm consists of the following procedures, splitting the digital images into binary images, predicting of each element of binary images being based on the theory of the conditional Markov processes with discrete states, and coding using any known algorithm (here, Huffman method is used). Results: To prove the efficiency of the proposed method, the compression of the Earth surface space pictures (group A) and photos (group B) is done. In each groupm, there were 50 onetype images. The known lossless compression algorithms such as PNG, JPEG-LS, JPEG 2000, BMF, Qlic, and ImageZero, are used as analogs. The obtained research results indicate that the proposed method performance and compression ratio comparable with analogues. Concluding Remarks: The suggested method has the following advantages: the capability of simultaneous binary images processing, the capability of digital images processing with digit capacity, and the lack of computational operations.

A New Approach in Digital Image Compression Using Unequal Error Protection (UEP)

This paper proposes a new algorithms for compression of digital images especially at the encoding stage of compressive sensing. The research consider the fact that a certain region of a given imagery is more important in most applications. The first algorithm proposed for the encoding stage of Compressive Sensing (CS) exploits the known structure of transform image coefficients. The proposed algorithm makes use of the unequal error protection (UEP) principle, which is widely used in the area of error control coding. The second algorithm which exploits the UEP principle to recover the more important part of an image with more quality while the rest part of the image is not significantly degraded. The proposed algorithm shown to be successful in digital image compression where images are represented in the spatial and transform domains. This new algorithm were recommended for use in image compression.