Image Compression Applications Research Articles

Effects of image compression on face image manipulation detection: A case study on facial retouching

Numerous methods have been introduced to reliably detect digital face image manipulations in the past. Of late, the generalisability of these schemes has been questioned in particular with respect to image post-processing. Image compression represents a post-processing which is frequently applied in diverse biometric application scenarios. Severe compression might erase digital traces of face image manipulation and hence hamper a reliable detection thereof. In this work, the effects of image compression on the face image manipulation detection are analysed. In particular, a case study on facial retouching detection under the influence of image compression is presented. To this end, ICAO-compliant subsets of two public face databases are used to automatically create a database containing more than 9000 retouched reference images together with unconstrained probe images. Subsequently, reference images are compressed applying JPEG and JPEG 2000 at compression levels recommended for face image storage in electronic travel documents. Novel detection algorithms utilising texture descriptors and deep face representations are proposed and evaluated in a single image and differential scenario. Results obtained from challenging cross-database experiments in which the analysed retouching technique is unknown during training yield interesting findings: (1) most competitive detection performance is achieved for differential scenarios employing deep face representations; (2) image compression severely impacts the performance of face image manipulation detection schemes based on texture descriptors while methods utilising deep face representations are found to be highly robust; (3) in some cases, the application of image compression might as well improve detection performance.

The Application of Color Image Compression Based on Discrete Wavelet Transform

The rapid progress in technology has been the use of color images that contain very large data and to transfer large information without loss of image information led to the creation of an important tool used to compress the color image, which is the modern wavelengths derived from polynomials, depending on the influencers (s,r) responsible for expansion, contraction and compensation in The parent function for obtaining the functions at each point depends on the influences responsible for the formation of the wavelet functions from which we derive the appropriate filter responsible for analyzing the color image and divide it into parameters that divide into two parts Approach and Details. In this work, new wavelets were used in order to find the exact solution to a number of important numerical problems in the field of mathematics. These methods are tested in the field of multi resolution analysis (MRA) for using in the field of image processing where it was used in the analysis and compression of the color image, calculating the most important image quality Mean Square Error (MSE) and Peak signal-to-noise ratio (PSNR). Standards and comparing the results with the results obtained using standard wavelets used in the Matlab program that was used in this work. In physics, the images of atoms were taken, random images were designed and designed with a size (256 x256) that were processed using modern wavelets.

Performance analysis of a new fractal compression method for medical images based on fixed partition

Applications of image compression have been gaining importance over the years in medical fields. The medical imaging modalities produced a large amount of information on all levels of hospital care. This information, in the form of images, needs to be stored for future references. While compressing the medical images, it is necessary to maintain high diagnostic quality with a high compression rate. Several image compression approaches have been developed towards the direction of the storage space problem in such a way that the doctors accurately and reliably diagnose the patient’s diseases from the reconstructed image after decompression. A high compression ratio is required to reduce the storage space due to the large size of medical images. Fractal image compression is a lossy technique to compress the image in a coded form instead of pixels and is differentiated by its long encoding time with a high compression ratio, resolution-independent, fast decoding, and self-similarity. The main purpose of this paper is to present a comparative performance study of the three coding schemes of fractal compression for grayscale medical images based on fixed partition. The first two coding schemes are based on the pixel-pattern measure and the third scheme is a proposed method based on the fractal dimension for complexity measure of range and domain blocks. The comparative study included encoding time, peak signal to noise ratio, and compression ratio, as a result, has been accomplished.

Analysis of power–accuracy trade‐off in digital signal processing applications using low‐power approximate adders

In recent years, approximate circuit design targeting the error-tolerant applications has gained significance. In this study, the authors propose a metric that ranks a stand-alone approximate adder in terms of power savings obtained for a given mean error distance/mean square error (MSE). The authors demonstrate that this ranking of approximate adders can be used in applications that contain adder trees and registers. In applications that also have accurate multipliers interspersed with adders, the authors find that certain types of approximations in the adders result in more power-efficient implementations of multipliers. Besides power savings, the other metrics of interest are noise floor and mean error in filtering applications and the compression achieved for a given peak signal-to-noise ratio (PSNR) in image compression applications. The authors also show that for the same overall MSE, there is a trade-off between noise floor and mean error. This makes it possible to classify these adders based on whether they result in an increased noise floor or a mean error for the same overall MSE. Furthermore, the authors discuss the effect of using an approximate discrete cosine transform block to meet the reduced PSNR requirements, on the overall compression levels and the trade-offs involved in the process.

Reversible Autoencoder: A CNN-Based Nonlinear Lifting Scheme for Image Reconstruction

Emerging deep learning approaches have facilitated image reconstruction at the expense of excessive model complexities and lack of theoretical guarantees of stability. In this paper, we propose a theoretically sound deep architecture, named reversible autoencoder (Rev-AE), from the perspective of well-developed frame theory for image reconstruction. Reversible blocks (Rev-Blocks) are developed as fundamental building blocks to realize nonlinear dimension invariant transform, up-sampling (with dimension growth) and down-sampling (with dimension reduction) in Rev-AE. The Rev-Block integrates CNN-based operators with the two-stream structure of lifting scheme to introduce nonlinearity and achieve reversibility. Therefore, Rev-AE only requires training and storage of encoder parameters. Furthermore, Rev-AE bridges existing nonlinear CNNs and well-established traditional signal processing tools to realize end-to-end optimized reversible networks with a guarantee of stability for image reconstruction for the first time. Theoretical analysis demonstrates that reversible blocks guarantee perfect reconstruction with derived frame bounds and are robust to noise perturbation under restricted energy propagation of noise. Moreover, we prove that Rev-AE achieves stable reconstruction with bounded projection angle under the presence of down-sampling and up-sampling. The stability and robustness of Rev-Blocks are also validated with numerical evaluations. Experimental results demonstrate that Rev-AE achieves comparable performance with halved parameters in comparison to recent leading methods (e.g., 3-D CNN based autoencoder and ISTA-Net$^+$) in the applications of image compression and compressive sensing.

Foveated JND Model Based on Stereo Vision and Its Application in Image Compression with Hardware Implementation

Foveated JND Model Based on Stereo Vision and Its Application in Image Compression with Hardware Implementation

Implementation and Evaluation of Vision-Based Sensor Image Compression for Close-Range Photogrammetry and Structural Health Monitoring.

Much research is still underway to achieve long-term and real-time monitoring using data from vision-based sensors. A major challenge is handling and processing enormous amount of data and images for either image storage, data transfer, or image analysis. To help address this challenge, this study explores and proposes image compression techniques using non-adaptive linear interpolation and wavelet transform algorithms. The effect and implication of image compression are investigated in the close-range photogrammetry as well as in realistic structural health monitoring applications. For this purpose, images and results from three different laboratory experiments and three different structures are utilized. The first experiment uses optical targets attached to a sliding bar that is displaced by a standard one-inch steel block. The effect of image compression in the photogrammetry is discussed and the monitoring accuracy is assessed by comparing the one-inch value with the measurement from the optical targets. The second application is a continuous static test of a small-scale rigid structure, and the last application is from a seismic shake table test of a full-scale 3-story building tested at E-Defense in Japan. These tests aimed at assessing the static and dynamic response measurement accuracy of vision-based sensors when images are highly compressed. The results show successful and promising application of image compression for photogrammetry and structural health monitoring. The study also identifies best methods and algorithms where effective compression ratios up to 20 times, with respect to original data size, can be applied and still maintain displacement measurement accuracy.

Image compression using adaptive multiresolution image decomposition algorithm

With the growth of modern digital technologies, demand for transmission multimedia and digital images, which require more storage space and transmission bandwidth, has been increased rapidly. Hence, developing new image compression techniques for reducing data size without degrading the quality of the image, has gained a lot of interest recently. In this study, an adaptive multiresolution image decomposition (AMID) algorithm is proposed and its application for image compression is explored. The developed algorithm is capable of decomposing an image along the vertical, horizontal, and diagonal directions using the pyramidal multiresolution scheme. Compared to the wavelet transform, the AMID can be used for decimation with the guarantees of perfect signal reconstruction. Furthermore, the application of the AMID for image compression is explored and its performance is compared with the state-of-the-art image compression techniques. The performance of compression method is evaluated using peak signal-to-noise ratio and compression ratio. Experimental results have shown promising performance compared with the results of using other image compression approaches.

Interactive Deep Colorization and its Application for Image Compression.

Recent methods based on deep learning have shown promise in converting grayscale images to colored ones. However, most of them only allow limited user inputs (no inputs, only global inputs, or only local inputs), to control the output colorful images. The possible difficulty lies in how to differentiate the influences of different inputs. To solve this problem, we propose a two-stage deep colorization method allowing users to control the results by flexibly setting global inputs and local inputs. The key steps include enabling color themes as global inputs by extracting K mean colors and generating K-color maps to define a global theme loss, and designing a loss function to differentiate the influences of different inputs without causing artifacts. We also propose a color theme recommendation method to help users choose color themes. Based on the colorization model, we further propose an image compression scheme, which supports variable compression ratios in a single network. Experiments on colorization show that our method can flexibly control the colorized results with only a few inputs and generate state-of-the-art results. Experiments on compression show that our method achieves much higher image quality at the same compression ratio when compared to the state-of-the-art methods.

An Improved Irreversible Fractal Scheme for Medical Image Compression

In this paper, an improved fractal image compression (FIC) based on peer adjacent scheme and domain classification was proposed. The proposed method has low computation cost since it contains no search operations, thus becoming fast irreversible fractal scheme. Comprehensive experiments on a standard test image and several types of digital radiology images revealed that the proposed method is competitive when compared to established quadtree-based FIC techniques. The novelty of the proposed method lies in the use of this improved domain classification and mapping strategy for accurate and more precise FIC encoding. The empirical result of standard test image suggests that the proposed method is more competitive compared to the established schemes and achieves better performance in terms the peak signal-to-noise ratio (PSNR) and compression time averaging at 27.27 dB and 6.88 s, respectively. Also, the proposed method obtains an efficient compression ratio with 16.13 compared to others. Additionally, experiments involving various medical image modalities confirmed the superiority of the proposed method for practical applications of medical image compression.

Algorithm-based fault tolerance for discrete wavelet transform implemented on GPUs

An important application for discrete wavelet transform (DWT) is the image compression such as the JPEG2000 Standard, and GPU is a good choice for the implementation of the DWT. For the application of image compression in the space environment, this paper has discussed the fault tolerance for the forward DWT (FDWT) implemented on GPUs, to deal with the silent data corruption (SDC) errors caused by single event upsets (SEUs). This paper has analyzed the matrix descriptions for the procedures of the forward DWT in the JPEG2000 Standard. Using the matrix descriptions, the propagation of SDC errors in the multi-level forward DWT has been analyzed, and an algorithm-based fault tolerance (ABFT) method has been proposed, in addition, by analyzing the relationship between the data type and the SDC errors, an on-line fast error detection and correction method has been proposed to suppress the propagation of the logical value overflow errors. Finally, the performance of the program with ABFT has been evaluated and the overhead of memory space caused by the checksum data has been further analyzed.

A new structure of spline wavelet transform based on adaptive directional lifting for efficient image coding

We present in this paper a modified structure of a polynomial spline wavelet transform based on adaptive directional lifting for image compression. The proposed method not only uses the polynomial splines as a tool for the construction of the appropriate filters seeing its efficiency as compared to other filters like the biorthogonal 9/7, but also adapts far better to the image-orientation features by carrying out a lifting-based prediction in local windows in the direction of high pixel correlation. The main purpose of this article is then to integrate the coefficients calculated by the best spline filter order into the adaptive directional lifting. The new method is designed to further reduce the magnitude of the high-frequency wavelet coefficients and preserve the detailed information of the original images more effectively. The numerical results demonstrate the efficiency of the proposed approach over the traditional lifting-based spline wavelet transform and the adaptive directional lifting with respect to both objective and subjective criteria for image compression applications.

Implementation of Vedic Multiplier and Floating Point Matrix Multiplier in Image Compression Applications

It is implemented into low Power Verilog Architecture to the area for digital images Process application, In the matrix multiplications are one of the key arithmetically operations. And the constructed into VLSI architecture for Low Power, High Speed & Lowarea, Matrices Multiplications designed into become rare. In the projects, is a simple work of fiction in Verilog architectures with Floating point matrix multiplier be presents. The designs into consider as Pseudo codes with the matrix multiplications, CSD multiplication algorithms with power reductions, Convention floating points as number formatting & Pipeline concept with as improves speeds. In the Floating point matrix multiplier design as appropriate with anyone orbitrary sizes of the matrix among the followed matrices rule. It is designed may also gives as higher precision outputs. The simulation result is perfect matched into the MATLAB result.

Low-Complexity Rate-Distortion Optimization of Sampling Rate and Bit-Depth for Compressed Sensing of Images.

Compressed sensing (CS) offers a framework for image acquisition, which has excellent potential in image sampling and compression applications due to the sub-Nyquist sampling rate and low complexity. In engineering practices, the resulting CS samples are quantized by finite bits for transmission. In circumstances where the bit budget for image transmission is constrained, knowing how to choose the sampling rate and the number of bits per measurement (bit-depth) is essential for the quality of CS reconstruction. In this paper, we first present a bit-rate model that considers the compression performance of CS, quantification, and entropy coder. The bit-rate model reveals the relationship between bit rate, sampling rate, and bit-depth. Then, we propose a relative peak signal-to-noise ratio (PSNR) model for evaluating distortion, which reveals the relationship between relative PSNR, sampling rate, and bit-depth. Finally, the optimal sampling rate and bit-depth are determined based on the rate-distortion (RD) criteria with the bit-rate model and the relative PSNR model. The experimental results show that the actual bit rate obtained by the optimized sampling rate and bit-depth is very close to the target bit rate. Compared with the traditional CS coding method with a fixed sampling rate, the proposed method provides better rate-distortion performance, and the additional calculation amount amounts to less than 1%.

Comprehensive Compact Phenomenological Modeling of Integrated Metal-Oxide Memristors

We present a comprehensive phenomenological model for the crossbar integrated metal-oxide continuous-state memristors. The model consists of static and dynamic equations, which are obtained by fitting a large amount of experimental data, collected on several hundred devices. Model describes the average devices’ I-V characteristics as well as their spatial (device-to-device) and temporal variations. Both static and dynamic equations are explicit, computationally inexpensive, and suitable for SPICE modeling. The model's predictive power is validated using experimental data, while its utility by simulating image compression and image classification applications, two practical representative applications of mixed-signal memristor-based hardware.

Impact of Spatial Dynamic Search With Matching Threshold Strategy on Fractal Image Compression Algorithm Performance: Study

Fractal image compression (FIC) is a very popular technique in image compression applications due to its simplicity and superior performance. However, it has a major drawback, which is the long encoding time. This is due to the requirement of performing huge similarity search for encoding each small portion in the image. Thus, reducing the search time of FIC while keeping the quality of reconstructed images at acceptable level is still an active research topic. Therefore, this paper has focused on studying the search problem of the conventional full-search FIC algorithm and the impact of employing a spatial dynamic search technique instead with the matching threshold strategy. Unlike the conventional full-search method that is a spatially static where the search starts from a fixed position (normally from the top-left corner of the image to the bottom-right corner) regardless of the position of the range block being encoding, the idea of the dynamic search method is simple, but effective, and it is based on starting the search from the closest domain block to the range block that needs to be encoded. These two search schemes are tested under different matching threshold values, in which the search is terminated whenever a domain block with an acceptable matching level is found. To make the study comprehensive, the test is performed for different image sizes and types, range block and partitioning step sizes, and quantization levels. The experimental results show the significant impact of using the dynamic search method instead of the conventional search method specifically when the threshold is large. For the best encoding parameters, the improvement amount that can be achieved is near to 90 % in terms of search reduction and 1.6 dB PSNR in terms of image quality.

Design of Faster Fractal Image Compression using Intensity Mismatch Threshold

Within the domain of image processing the applications of the fractal image compression through the affine transform is rarely implemented. The major drawback it consists is its time consumptions in simulations as well as in hardware implementation. The reason behind this is its comparatively matching the range blocks with the varied number of domain blocks. Additionally, the compression ration obtained from this sets of techniques are much more that the other similar working techniques. In order to overcome, this drawback, here a concept of intensity mismatch searching techniques is intruded within this particular paper. While in the mean time, it only concluded in much reduce in time of searching for similar domain block form a pool. And also this technique does not affect within the compression ratio and PSNR measurement form the retrieved image. Further benefitted in hardware applications where the matters for time consumptions is a major factor.

An improved saliency detection method based on hypercomplex Fourier transform

Saliency detection is a challenging direction in the field of computer vision. It can help us quickly obtain important information of image and video, and plays an important role in image compression, image segmentation, target recognition and other applications. Based on the Hypercomplex Fourier Transform (HFT) model, median filter and threshold segmentation are employed to obtain the image salient region, which solves the problem that the boundary is not clear and multiple targets cannot be separated, and achieves more accurate and stable detection effect.

Homogeneous Image Compression Techniques with the Shannon-Fano Algorithm

Compression is a field that needed at this time where increasingly high-tech digital and computing makes it possible to process data in a size that is large enough like multimedia. Compression needed to keep pressing the storage media consumption of data and information stored on computer media. The Shannon-Fano compression algorithm is one of the well-known compression algorithms and is useful in saving data storage space. Shannon-Fano compression algorithms can be performed on text and digital images. In this study applying the Shannon-Fano method to homogeneous digital images using applications created with the MATLAB Program. The product of this research will be tested using several images taken using a webcam. The product tested to find out whether the coding used is correct or has errors. The product will be successful if it can reduce the size of the compressed image from the original image. Homogeneous images that have tested using the Shannon-Fano image compression application have an average compression ratio value of 52%. The size of the compression results reaches half of the original image. The Shannon-Fano compression algorithm detects the number of times that characters appear in each experiment, then the coding of the frequency of characters appearing in binary numbers.

Image compression system with an optimisation of compression ratio

The fundamental goal of image data compression is to set an optimal compression ratio while maintaining an acceptable reproduction quality. This study describes the principles of design of image compression system that automatically sets an optimal compression ratio for particular image content by identifying the image compression method while maintaining a tolerable reproduction quality. The proposed image compression system employs subjective and objective criteria for an assessment of the quality of the image compression system. The supervised artificial neural network is used to identify a compression method among different compression techniques by using subjective criterion. An objective criterion is used to determine an optimal compression ratio, which is calculated by using linear regression analysis that establishes analytical expression between a compression ratio, a property of an image and a reconstructed quality of an image. It was proved that the system is an effective tool for medical image compression applications.