Standard Compression Techniques Research Articles

Deep Learning-Based model for Medical Image Compression

Efficient compression algorithms are required to handle the growing amount of medical picture data, ensuring that storage and transmission requirements are met without compromising diagnostic quality. This research presents a hybrid image compression framework that integrates deep learning alongside standard lossless compression techniques. A convolutional autoencoder (CAE) learns a compact representation of medical images, which are subsequently compressed using the Brotli algorithm. Our technique beats conventional approaches, like JPEG, JPEG2000, and wavelet-based ones, according to an analysis of a brain MRI dataset. It maintains competitive compression ratios while producing higher (PSNR) and (MSE), indicating higher picture integrity and low information loss. To strike a good balance between the critical need for accurate diagnosis and the economical use of resources, this study offers a possible method for compressing medical images.

Iris Data Compression Based on Hexa-Data Coding

Iris research is focused on developing techniques for identifying and locating relevant biometric features, accurate segmentation and efficient computation while lending themselves to compression methods. Most iris segmentation methods are based on complex modelling of traits and characteristics which, in turn, reduce the effectiveness of the system being used as a real time system. This paper introduces a novel parameterized technique for iris segmentation. The method is based on a number of steps starting from converting grayscale eye image to a bit plane representation, selection of the most significant bit planes followed by a parameterization of the iris location resulting in an accurate segmentation of the iris from the original image. A lossless Hexadata encoding method is then applied to the data, which is based on reducing each set of six data items to a single encoded value. The tested results achieved acceptable saving bytes performance for the 21 iris square images of sizes 256x256 pixels which is about 22.4 KB on average with 0.79 sec decompression average time, with high saving bytes performance for 2 iris non-square images of sizes 640x480/2048x1536 that reached 76KB/2.2 sec, 1630 KB/4.71 sec respectively, Finally, the proposed promising techniques standard lossless JPEG2000 compression techniques with reduction about 1.2 and more in KB saving that implicitly demonstrating the power and efficiency of the suggested lossless biometric techniques.

Comparison of hemodynamic effects of chest compression delivered via machine or human in asphyxiated piglets

BackgroundHigh-quality chest compressions (CC) are an important factor of neonatal resuscitation. Mechanical CC devices may provide superior CC delivery and improve resuscitation outcomes. We aimed to compare the hemodynamic effects of CC delivered by machine and human using a neonatal piglet model.MethodsTwelve asphyxiated piglets were randomized to receive CC during resuscitation using an automated mechanical CC device (“machine”) or the two-thumb encircling technique (“human”). CC was superimposed with sustained inflations.ResultsTwelve newborn piglets (age 0–3 days, weight 2.12 ± 0.17 kg) were included in the study. Machine-delivered CC resulted in an increase in stroke volume, and minimum and maximum rate of left ventricle pressure change (dp/dtmin and dp/dtmax) compared to human-delivered CC.ConclusionsDuring machine-delivered CC, stroke volume and left ventricular contractility were significantly improved. Mechanical CC devices may provide improved cardiopulmonary resuscitation outcomes in neonatal cardiac arrest induced by asphyxia.ImpactMachine chest compression leads to changes in hemodynamic parameters during resuscitation of asphyxiated neonatal piglets, namely greater stroke volume and left ventricular contractility, compared with standard two-thumb compression technique. Mechanical chest compression devices may provide improved cardiopulmonary resuscitation outcomes in neonatal and pediatric asphyxia-induced cardiac arrest.

Performance analysis of wireless transmission of compressed images using DCT‐OFDMA system with different compression schemes

AbstractTransmitting images over an Orthogonal Frequency Division Multiplexing Access (OFDMA) system poses a significant challenge. The process entails sending a substantial amount of data, which consumes a significant amount of bandwidth. Consequently, compressing the transmitted image becomes essential to reduce the required bandwidth. The aim of this paper is to examine and analyse the wireless transmission of a compressed image via Discrete Cosine Transform (DCT‐OFDMA). A comparison is made with Discrete Fourier Transform (DFT‐OFDMA), across various subcarrier mapping schemes (localized and inter‐leaved), and different modulation schemes (16 Quadrature Amplitude Modulation (16QAM) and Quadrature Phase Shift Keying (QPSK)) using vehicular A, Stanford University Interim (SUI3), and uniform channel models. To evaluate the performance of the system, the minimum Signal‐to‐Noise Ratio (SNR) necessary to recover the transmitted compressed image is calculated. This work considers nine standard compression techniques. The results are carried out using the MATLAB simulator. According to the simulation results, the minimum SNR required to recover the transmitted compressed image was found to be 19 dB. This result was achieved when using Discrete Cosine Transform‐Loaclized‐Orthogonal Frequency Division Multiplexing Access (DCT‐LOFDMA) with QPSK modulation and set partitioning in hierarchical trees (SPIHT) compression method over the SUI3 channel model. Moreover, it was observed that the DCT‐LOFDMA and DFT‐IOFDMA systems attained equal SNR while utilizing the SPIHT_3D compression technique and QPSK modulation on the SUI3 channel model. Overall, the results suggest that the performance of DCT‐based localized OFDMA is somewhat superior to DFT‐based localized OFDMA, particularly when utilizing the SUI3 channel model and the QPSK modulation scheme. Therefore, it is feasible to transmit and receive a compressed image effectively over an OFDMA system with DCT.

Adaptive Polynomial Coding of Multi-base Hybrid Compression

With increasing demand for the intensive use of images, especially linked to online applications as well as the massive, continuous revolution of mobile phone technology, the need has emerged for efficient, standard image compression techniques that ensure simplicity and speed. These must be compatible with user needs, but also meet the challenges of improving compression techniques. Polynomial coding is one such techniques still under development, based on a modelling concept of deterministic and probabilistic coding bases. This paper introduces a new mathematical iterative polynomial model to represent both coding bases. The model proposes an efficient hybrid way where coefficients are represented as lossless while residuals are presented as a lossy but with minimum loss, which ensures effective performance in terms of compression ratios and quality. Results show that while the technique has some limitations, the proposed system achieves equivalent compression ratios as the standard JPEG technique, but with superior quality for the same compression ratio.

Compact Preservation of Scrambled CD-Rom Data

When preserving CD-ROM discs, data sectors are often read in a so-called “scrambled mode” in order to preserve as much data as possible. This scrambled data is later unscrambled and further processed into a standard CD-ROM disc image. The process of converting the scrambled data into a standard CD-ROM disc image is potentially lossy, but standard CD-ROM disc images exhibit much higher software compatibility and have greater usability compared to the scrambled data from which they are derived. Consequently, for preservation purposes, it is often necessary to store both the scrambled data and the corresponding standard disc image, resulting in greatly increased storage demands compared to storing just one or the other. Here, a method that enables compact storage of scrambled data alongside the corresponding (unscrambled) standard CD-ROM disc image is introduced. The method produces a compact representation of the scrambled data that is derived from the unscrambled disc image. The method allows for (1) storage of the standard unscrambled disc image in unmodified form, (2) easy reconstruction of the scrambled data as needed, and (3) a substantial space savings (in the typical case) compared to storing the scrambled data using standard data compression techniques.

Three-Dimensional Adaptive Image Compression Concept for Medical Imaging: Application to Computed Tomography Angiography for Peripheral Arteries.

Advances in computed tomography (CT) have resulted in a substantial increase in the size of datasets. We built a new concept of medical image compression that provides the best compromise between compression rate and image quality. The method is based on multiple contexts and regions-of-interest (ROI) defined according to the degree of clinical interest. High priority areas (primary ROIs) are assigned a lossless compression. Other areas (secondary ROIs and background) are compressed with moderate or heavy losses. The method is applied to a whole dataset of CT angiography (CTA) of the lower extremity vasculature. It is compared to standard lossy compression techniques in terms of quantitative and qualitative image quality. It is also compared to standard lossless compression techniques in terms of image size reduction and compression ratio. The proposed compression method met quantitative criteria for high-quality encoding. It obtained the highest qualitative image quality rating score, with a statistically significant difference compared to other methods. The average compressed image size was up to 61% lower compared to standard compression techniques, with a 9:1 compression ratio compared with original non-compressed images. Our new adaptive 3D compression method for CT images can save data storage space while preserving clinically relevant information.

Reduction of satellite images size in 5G networks using machine learning algorithms

The high data volume of multispectral satellite images is compressed for better visual perception without loss of image and statistical properties of the local or global image to provide superior information for obtaining effective results using 5G networks. This compression is a technique applied to remote sensing applications to analyse the data for prediction or forecasting the real-time applications by remote sensing applications like IoT and data transmission over 5G wireless networks. The extensive data images have multiple bands, which contain earth surface/object information with various frequencies. It is difficult to handle this extensive data for processing data. The compression is mandatory to avoid this complexity by removing redundancy data, unnecessary pixel information and non-visual redundancy data between bands. There are various standard compression techniques are available like JPEG 2000, Wavelet and DCT methods. The proposed method is implemented with a combination of intra coding and machine learning algorithm. The standard compression technique does not give better results due to degradation of pixels, lack of spatial and spectral information. This paper enriches progressive results by reduced satellite images for transmission of data in IoT and 5G wireless networks, in which qualitative results are compared by standard compression technique with suitable parameters.

Art Image Compression Based on Lossless LZW Hashing Ciphering Algorithm

Color image compression is a good way to encode digital images by decreasing the number of bits wanted to supply the image. The main objective is to reduce storage space, reduce transportation costs and maintain good quality. In current research work, a simple effective methodology is proposed for the purpose of compressing color art digital images and obtaining a low bit rate by compressing the matrix resulting from the scalar quantization process (reducing the number of bits from 24 to 8 bits) using displacement coding and then compressing the remainder using the Mabel ZF algorithm Welch LZW. The proposed methodology maintains the quality of the reconstructed image. Macroscopic and quantitative experimental results on technical color images show that the proposed methodology gives reconstructed images with a high PSNR value compared to standard image compression techniques.

Benefiting from Duplicates of Compressed Data: Shift-Based Holographic Compression of Images

Storage systems often rely on multiple copies of the same compressed data, enabling recovery in case of binary data errors, of course, at the expense of a higher storage cost. In this paper, we show that a wiser method of duplication entails great potential benefits for data types tolerating approximate representations, like images and videos. We propose a method to produce a set of distinct compressed representations for a given signal, such that any subset of them allows reconstruction of the signal at a quality depending only on the number of compressed representations utilized. Essentially, we implement the holographic representation idea, where all the representations are equally important in refining the reconstruction. Here, we propose to exploit the shift sensitivity of common compression processes and generate holographic representations via compression of various shifts of the signal. Two implementations for the idea, based on standard compression methods, are presented: the first is a simple, optimization-free design. The second approach originates in a challenging rate-distortion optimization, mitigated by the alternating direction method of multipliers (ADMM), leading to a process of repeatedly applying standard compression techniques. Evaluation of the approach, in conjunction with the JPEG2000 image compression standard, shows the effectiveness of the optimization in providing compressed holographic representations that, by means of an elementary reconstruction process, enable impressive gains of several dBs in PSNR over exact duplications.

RR-DnCNN v2.0: Enhanced Restoration-Reconstruction Deep Neural Network for Down-Sampling-Based Video Coding.

Integrating deep learning techniques into the video coding framework gains significant improvement compared to the standard compression techniques, especially applying super-resolution (up-sampling) to down-sampling based video coding as post-processing. However, besides up-sampling degradation, the various artifacts brought from compression make super-resolution problem more difficult to solve. The straightforward solution is to integrate the artifact removal techniques before super-resolution. However, some helpful features may be removed together, degrading the super-resolution performance. To address this problem, we proposed an end-to-end restoration-reconstruction deep neural network (RR-DnCNN) using the degradation-aware technique, which entirely solves degradation from compression and sub-sampling. Besides, we proved that the compression degradation produced by Random Access configuration is rich enough to cover other degradation types, such as Low Delay P and All Intra, for training. Since the straightforward network RR-DnCNN with many layers as a chain has poor learning capability suffering from the gradient vanishing problem, we redesign the network architecture to let reconstruction leverages the captured features from restoration using up-sampling skip connections. Our novel architecture is called restoration-reconstruction u-shaped deep neural network (RR-DnCNN v2.0). As a result, our RR-DnCNN v2.0 outperforms the previous works and can attain 17.02% BD-rate reduction on UHD resolution for all-intra anchored by the standard H.265/HEVC. The source code is available at https://minhmanho.github.io/rrdncnn/.

New Quality Metric for Compressed Images

The field of image processing has several applications in our daily life. The image quality can be affected by a wide variety of deformations during image acquisition, transmission, compression, etc. Image compression is one of the applications where the quality of the image plays an important role since it can be used to evaluate the performance of various image compression techniques. Many image quality assessment metrics have been proposed. This paper proposes a new metric to assess the quality of compressed images. The principle idea of this metric is to estimate the amount of lost information during image compression process using three components: error magnitude, error location and error distribution. We denote this metric as MLD, which combines the objective assessment (error magnitude) and the subjective assessment (error location and error distribution). First, the metric is used to estimate the quality of compressed images using the JPEG algorithm as this is a standard lossy image compression technique. Then, the metric is used to estimate the quality of compressed images using other compression techniques. The results illustrate that the proposed quality metric is correlated with the subjective assessment better than other well-known objective quality metrics such as SSIM, MSE and PSNR. Moreover, using the proposed metric the JPEG2000 algorithm produces better quality results as compared to the JPEG algorithm especially for higher compression ratios

Optimal Management of the Multimedia Application

The need for multimedia applications has increased over the years. Due to this issue, image and video compression becoming an essential problem in reducing time and data storage. Fractal Image Compression (FIC) is a compression technique which results in loss, and we can have large amounts of compression. This scheme works by classifying an image into blocks and uses Contractive Mapping. Some of FICs advantages can be expressed as follows: comparing with other standard compression techniques, it has a good interchange between PNSR and compression ratio and it zooms the image without reducing the quality due to its resolution independent nature. The main FICs drawback, is its complexity in encoding and lack of speed.

Survey on Fractal image compression

The need for multimedia applications has increased over the years. Due to this issue, image and video compression becoming an essential problem in reducing time and data storage. Fractal Image Compression (FIC) is a compression technique which results in loss, and we can have large amounts of compression. This scheme works by classifying an image into blocks and uses Contractive Mapping. Some of FICs advantages can be expressed as follows: comparing with other standard compression techniques, it has a good interchange between PNSR and compression ratio and it zooms the image without reducing the quality due to its resolution independent nature. The main FICs drawback, is its complexity in encoding and lack of speed.

Launching Vehicle Acoustic Data Compression Study Using Lossy Audio Formats

The field of data compression has evolved over the last decades. In this way, several techniques to reduce the amount of acquired data from the sensor required to be transmitted have been developed. Those techniques are usually classified by lossless or lossy, where, for the lossless techniques, all acquired data is recovered, while the lossy techniques introduce errors to these data. Each of these techniques presents advantages and drawbacks, being the analyst responsible for choosing the appropriate technique for a specific application. This work presents a comparative study using lossy audio formats to be applied on a launch vehicle on-board acoustic data. The Opus format achieved a higher compression rate in comparison with standard compression techniques by saving up to 254 times the required amount of data to be transmitted through a telemetry link on launcher vehicle, and the lowest discrepancy from original data measured by the mean square error metric.

Design and Development of a Very Large Scale Integrated Circuit Architecture for a Digital Image Compression and Realization on Field Programmable Gate Array

The domain of image signal processing, image compression is the significant technique, which is mainly invented to reduce the redundancy of image data in order to able to transmit the image pixels with high quality resolution. The standard image compression techniques like losseless and lossy compression technique generates high compression ratio image with efficient storage and transmission requirement respectively. There are many image compression technique are available for example JPEG, DWT and DCT based compression algorithms which provides effective results in terms of high compression ratio with clear quality image transformation. But they have more computational complexities in terms of processing, encoding, energy consumption and hardware design. Thus, bringing out these challenges, the proposed paper considers the most prominent research papers and discuses FPGA architecture design and future scope in the state of art of image compression technique. The primary aim to investigate the research challenges toward VLSI designing and image compression. The core section of the proposed study includes three folds viz standard architecture designs, related work and open research challenges in the domain of image compression.

Design and Development of a Very Large Scale Integrated Circuit Architecture for a Digital Image Compression and Realization on Field Programmable Gate Array

The domain of image signal processing, image compression is the significant technique, which is mainly invented to reduce the redundancy of image data in order to able to transmit the image pixels with high quality resolution. The standard image compression techniques like losseless and lossy compression technique generates high compression ratio image with efficient storage and transmission requirement respectively. There are many image compression technique are available for example JPEG, DWT and DCT based compression algorithms which provides effective results in terms of high compression ratio with clear quality image transformation. But they have more computational complexities in terms of processing, encoding, energy consumption and hardware design. Thus, bringing out these challenges, the proposed paper considers the most prominent research papers and discuses FPGA architecture design and future scope in the state of art of image compression technique. The primary aim to investigate the research challenges toward VLSI designing and image compression. The core section of the proposed study includes three folds viz standard architecture designs, related work and open research challenges in the domain of image compression.

A proposed multi-image compression technique

Image compression is a process that reduces memory space required to store an image. The image compression techniques are broadly classified into two categories a) Lossless technique b) Lossy technique. Lossy compression technique achieves higher result, but due to data loss it may cause spatial in consistency, blocking artifact, quantization noise in the decompressed image degrading the image quality. Most of the existing compression techniques are applicable on single image separately. In this study an image compression method is proposed where multiple images of the same size are combined together and compressed to achieve higher compression ratio while keeping image quality as close as standard image compression techniques. Luminance channel of each image is compressed separately using Vector Quantization (VQ) algorithm while two chrominance channels, Cb and Cr of all images are combined into a three dimensional matrix that forms training vector. Clustering is applied on the training vector to get the initial color representatives. Thus, for the chrominance channels of n number of images, the proposed method generates one index matrix and one centroid matrix of size 256×2× n where 256 is the number of clusters. This centroid matrix contains one 256×2 dimensional centroid matrix for each individual image. This 256×2 matrix contains centroid of each cluster. The centroids of each and every cluster of an image are updated individually using optimization technique to get a better centroid (Cb, Cr) pair. This process updates the color representative pair of Cb and Cr further. This method has been applied on standard images in literature and images collected from UCID v. 2 color image database. Experimental results are analyzed in terms of PSNR and space reduction. Experimental results show that the proposed method achieves a higher compression ratio retaining almost similar image information as other standard lossy compression algorithm.

Анализ методов сжатия стереоскопических изображений

Virtual Reality (VR) and Augmented Reality (AR) Head-Mounted Displays (HMDs) have been emerging in the last years and they are gaining an increased popularity in many industries. HMDs are generally used in entertainment, social interaction, education, but their use for work is also increasing in domains such as medicine, modeling and simulation. Despite the recent release of many types of HMDs, two major problems are hindering their widespread adoption in the mainstream market: the extremely high costs and the user experience issues [1]. The illusion of a 3D display in HMDs is achieved with a technique called stereoscopy. Applications of stereoscopic imagining are such that data transfer rates and—in mobile applications—storage quickly become a bottleneck. Therefore, efficient image compression techniques are required. Standard image compression techniques are not suitable for stereoscopic images due to the discrete differences that occur between the compressed and uncompressed images. The issue is that the loss in lossy image compression may blur the minute differences between the left-eye and right-eye images that are crucial in establishing the illusion of 3D perception. However, in order to achieve more efficient coding, there are various coding techniques that can be adapted to stereoscopic images. Stereo image compression techniques that can be found in the literature utilize discrete Wavelet transformation and the morphological compression algorithm applied to the transform coefficients. This paper provides an overview and comparison of available techniques for the compression of stereoscopic images, as there is still no technique that is accepted as best for all criteria. We want to test the techniques with users who would actually be potential users of HMDs and therefore would be exposed to these techniques. Also, we focused our research on low-priced, consumer grade HMDs which should be available for larger population.

Optimized Pre-Compensating Compression.

In imaging systems, following acquisition, an image/ video is transmitted or stored and eventually presented to human observers using different and often imperfect display devices. While the resulting quality of the output image may severely be affected by the display, this degradation is usually ignored in the preceding compression. In this paper we model the sub-optimality of the display device as a known degradation operator applied on the decompressed image/video. We assume the use of a standard compression path, and augment it with a suitable pre-processing procedure, providing a compressed signal intended to compensate the degradation without any post-filtering. Our approach originates from an intricate rate-distortion problem, optimizing the modifications to the input image/video for reaching best end-to-end performance. We address this seemingly computationally intractable problem using the alternating direction method of multipliers (ADMM) approach, leading to a procedure in which a standard compression technique is iteratively applied. We demonstrate the proposed method for adjusting HEVC image/video compression to compensate post-decompression visual effects due to a common type of displays. Particularly, we use our method to reduce motion-blur perceived while viewing video on LCD devices. The experiments establish our method as a leading approach for preprocessing high bit-rate compression to counterbalance a postdecompression degradation.