Image Compression Research Articles

A Performance Study of Different Approaches of Digital Image Compression Techniques

A Performance Study of Different Approaches of Digital Image Compression Techniques

Spectral properties of bright deposits in permanently shadowed craters on Ceres

Context. Bright deposits in permanently shadowed craters on Ceres are thought to harbor water ice. However, the evidence for water ice presented thus far is indirect. Aims. We aim to directly detect the spectral characteristics of water ice in bright deposits present in permanently shadowed regions (PSRs) in polar craters on Ceres. Methods. We analyzed narrowband images of four of the largest shadowed bright deposits acquired by the Dawn Framing Camera to reconstruct their reflectance spectra, carefully considering issues such as in-field stray light correction and image compression artifacts. Results. The sunlit portion of a polar deposit known to harbor water ice has a negative (blue) spectral slope of −58 ± 12% µm−1 relative to the background in the visible wavelength range. We find that the PSR bright deposits have similarly blue spectral slopes, consistent with a water ice composition. Based on the brightness and spectral properties, we argue that the ice is likely present as particles of high purity. Other components such as phyllosilicates may be mixed in with the ice. Salts are an unlikely brightening agent given their association with cryovolcanic processes, of which we find no trace. Conclusions. Our spectral analysis strengthens the case for the presence of water ice in permanently shadowed craters on Ceres.

Multirate Progressive Entropy Model for Learned Image Compression

Multirate Progressive Entropy Model for Learned Image Compression

Exploiting Four-Dimensional Chaotic Systems With Dissipation and Optimized Logical Operations for Secure Image Compression and Encryption

Exploiting Four-Dimensional Chaotic Systems With Dissipation and Optimized Logical Operations for Secure Image Compression and Encryption

Wavelet-based Filter Bank Approach for Image Compression and Enhancement in Alzheimer\'s Disease Diagnosis Using Medical Imaging

Abstract: This research presents a wavelet-based filter bank approach for image compression and enhancement in the context of Alzheimer's disease diagnosis using medical imaging. The proposed system leverages the PyWavelets and OpenCV libraries in Python to implement Discrete Wavelet Transform (DWT) and Inverse Discrete Wavelet Transform (IDWT) algorithms. The primary objective is to develop an efficient image compression technique while preserving visual quality and enhancing relevant features for improved diagnostic accuracy. The system utilizes filter banks to process wavelet coefficients, enabling selective enhancement of patterns or biomarkers associated with Alzheimer's disease. Results show that the decompressed images closely resemble the originals, with low Mean Squared Error (MSE) and high Peak Signal-to-Noise Ratio (PSNR) values. Visual inspection and quantitative metrics confirm the successful preservation of image quality and structural details. The wavelet coefficients visualization highlights the captured significant features crucial for reconstruction

Improved Sparse Representation of Image from Inferred Angles of Steerable Wavelet

Objectives: Presenting images with sparse coefficients has a wide variety of real-time applications in compressive sensing. However, sparse representations of images present challenges due hidden similarities in the higher order moments. Literature suggests that the applications that involve natural images present a high level of similarity. Steerable basis, due to their rotational invariant property, have shown potential in sparse representation of natural images. Hence, the objective of the proposed study is to identify steerable basis that maximize the sparse representation of natural images. Method: Prior studies have used the angle of steerable basis either from the random assignment or derived from Hough transform. In this study, we propose the selection of steerable basis angle derived from maximum a prior method. Exploiting a steerable basis for better sparse representation requires the knowledge of proper steerable angles. Hence, we propose using MAP learning approach to identify this angle. Findings: The proposed method resulted in optimal steerable angle without the need for calculation of Hough Transform. In addition, the method also resulted in almost 10 percent improvement in sparse representation as indicated by higher Kurtosis. Novelty: We compare the measure of sparsity to evaluate the effectiveness of the proposed method. The results indicate the optimal sparsity from the proposed method as indicated from the maximum values of kurtosis compared to the previous related methods. In addition, the proposed method relaxes the requirement of manipulating Hough transform for optimal steerable angle. Keywords: Sparsity, Steerable Basis, Wavelet Pyramid Structure, Image Compression, Hough Transform

A lossy compression algorithm for data basis images based on DCT decomposition and diffuse representation

In this manuscript, we present a novel approach for compressing a database of images. Initially, we introduce a new representation that effectively reduces the data required to represent the image, enhancing the efficiency of subsequent compression steps. We apply the Discrete Cosine Transform (DCT) to the new representation and perform thresholding at different levels for a selected image. The DCT is known for concentrating image energy into a few coefficients, making it ideal for compression. By calculating the Peak Signal-to-Noise Ratio (PSNR) variation of the reconstructed image and evaluating the percentage of non-zero coefficients after thresholding, we plot the corresponding curve. These calculations provide insights into the quality of the reconstructed image, ensuring minimal degradation from compression. We repeat the steps with the Discrete Wavelet Transform (DWT), known for its multi-resolution analysis, to compare its performance against the DCT. The curves help us observe which process allows for image reconstruction with only a small percentage of coefficients, achieving significant data reduction. This comparative analysis determines the most effective method for various types of images. After analyzing the digital results from these curves, we generate and discuss the reconstructed image outcomes in detail. The digital and visual results presented at the conclusion demonstrate the robustness of the proposed strategy. We also explore potential applications in fields such as medical imaging, remote sensing, and multimedia storage, highlighting its versatility. Our findings suggest that the novel representation combined with DCT is highly effective, offering substantial storage space savings while maintaining image quality. These results are significant for theoretical research and practical implementations, providing a foundation for future advancements in image compression technologies.

Development of Power-Delay Product Optimized ASIC-Based Computational Unit for Medical Image Compression

The proliferation of battery-operated end-user electronic devices due to technological advancements, especially in medical image processing applications, demands low power consumption, high-speed operation, and efficient coding. The design of these devices is centered on the Application-Specific Integrated Circuits (ASIC), General Purpose Processors (GPP), and Field Programmable Gate Array (FPGA) frameworks. The need for low-power functional blocks arises from the growing demand for high-performance computational units that are part of high-speed processors operating at high clock frequencies. The operational speed of the processor is determined by the computational unit, which is the workhorse of high-speed processors. A novel approach to integrating Very Large-Scale Integration (VLSI) ASIC design and the concepts of low-power VLSI compatible with medical image compression was embraced in this research. The focus of this study was the design, development, and implementation of a Power Delay Product (PDP) optimized computational unit targeted for medical image compression using ASIC design flow. This stimulates the research community’s quest to develop an ideal architecture, emphasizing on minimizing power consumption and enhancing device performance for medical image processing applications. The study uses area, delay, power, PDP, and Peak Signal-to-Noise Ratio (PSNR) as performance metrics. The research work takes inspiration from this and aims to enhance the efficiency of the computational unit through minor design modifications that significantly impact performance. This research proposes to explore the trade-off of high-performance adder and multiplier designs to design an ASIC-based computational unit using low-power techniques to enhance the efficiency in power and delay. The computational unit utilized for the digital image compression process was synthesized and implemented using gpdk 45 nm standard libraries with the Genus tool of Cadence. A reduced PDP of 46.87% was observed when the image compression was performed on a medical image, along with an improved PSNR of 5.89% for the reconstructed image.

Compression and encryption for remote sensing image based on PSO-BP and 2D-MCCM

In response to the large size of remote sensing images and the limitations of existing image compression and encryption algorithms, this paper proposes a novel compression and encryption algorithm. The proposed algorithm utilizes a new type of memristive chaotic mapping in combination with PSO-BP neural networks and multi-threaded parallelism. Specifically, the proposed novel two-dimensional memristive chaotic mapping involves a combination of new memristors based on HP memristors and Cubic chaotic mapping. Compared to existing chaotic systems, this method exhibits stronger randomness and hyperchaotic characteristics. Additionally, to improve the reconstruction accuracy of compressed images, a traditional BP neural network with an added hidden layer is combined with the PSO algorithm for image compression and reconstruction. Furthermore, to enhance the encryption efficiency of remote sensing images, a multi-threaded parallel encryption method is employed, enabling simultaneous permutation within and among threads. Experimental results demonstrate that the proposed algorithm achieves good compression reconstruction accuracy, excellent encryption performance, and resistance to attacks.

Intelligent Image Compression Model on the Basis of Wavelet Transform and Optimized Fuzzy C-Means-Based Vector Quantisation

Intelligent Image Compression Model on the Basis of Wavelet Transform and Optimized Fuzzy C-Means-Based Vector Quantisation

BPG-Based Lossy Compression of Three-Channel Remote Sensing Images with Visual Quality Control

A tendency to increase the number of acquired remote sensing images and to make their average size larger has been observed. To manage such data, compression is needed, and lossy compression is often preferable. Since lossy compression introduces distortions, this results in worse classification and object detection. Therefore, lossy compression must be controlled, i.e., the introduced distortions must be under a certain limit. The distortions and the limit can be characterized by different metrics (quantitative criteria). Here, we consider the case of using the HaarPSI metric, which has a very high correlation with visual quality and human attention (saliency map), for three-channel optical band images compressed by the better portable graphics (BPG) encoder, one of the best modern compression techniques. We analyze a two-step procedure of providing a desired visual quality and show its peculiarities for the modes 4:4:4, 4:2:2, and 4:2:0 of image compression. We show how the HaarPSI metric relates to other known metrics of image visual quality and thresholds of distortion visibility. It is demonstrated that the two-step procedure provides about three times better accuracy in providing the desired visual quality compared to the fixed setting of parameter Q that controls compression for the BPG encoder. The provided accuracy is close to the reachable limit determined by the integer value setting of the Q parameter. We also briefly analyze the influence of compression on the classification accuracy of real-life remote sensing data.

FPWT: Filter pruning via wavelet transform for CNNs

The enormous data and computational resources required by Convolutional Neural Networks (CNNs) hinder the practical application on mobile devices. To solve this restrictive problem, filter pruning has become one of the practical approaches. At present, most existing pruning methods are currently developed and practiced with respect to the spatial domain, which ignores the potential interconnections in the model structure and the decentralized distribution of image energy in the spatial domain. The image frequency domain transform method can remove the correlation between image pixels and concentrate the image energy distribution, which results in lossy compression of images. In this study, we find that the frequency domain transform method is also applicable to the feature maps of CNNs. The filter pruning via wavelet transform (WT) is proposed in this paper (FPWT), which combines the frequency domain information of WT with the output feature map to more obviously find the correlation between feature maps and make the energy into a relatively concentrated distribution in the frequency domain. Moreover, the importance score of each feature map is calculated by the cosine similarity and the energy-weighted coefficients of the high and low frequency components, and prune the filter based on its importance score. Experiments on two image classification datasets validate the effectiveness of FPWT. For ResNet-110 on CIFAR-10, FPWT reduces FLOPs and parameters by more than 60.0 % with 0.53 % accuracy improvement. For ResNet-50 on ImageNet, FPWT reduces FLOPs by 53.8 % and removes parameters by 49.7 % with only 0.97 % loss of Top-1 accuracy.

Entropy-based guidance of deep neural networks for accelerated convergence and improved performance

Neural networks have dramatically increased our capacity to learn from large, high-dimensional datasets across innumerable disciplines. However, their decisions are not easily interpretable, their computational costs are high, and building and training them are not straightforward processes. To add structure to these efforts, we derive new mathematical results to efficiently measure the changes in entropy as fully-connected and convolutional neural networks process data. By measuring the change in entropy as networks process data effectively, patterns critical to a well-performing network can be visualized and identified. Entropy-based loss terms are developed to improve dense and convolutional model accuracy and efficiency by promoting the ideal entropy patterns. Experiments in image compression, image classification, and image segmentation on benchmark datasets demonstrate these losses guide neural networks to learn rich latent data representations in fewer dimensions, converge in fewer training epochs, and achieve higher accuracy.

Quantum Image Compression: Fundamentals, Algorithms, and Advances

Quantum Image Compression: Fundamentals, Algorithms, and Advances

A novel binary quantizer for variational autoencoder-based image compressor

Neural network-based encoder and decoder are one of the emerging techniques for image compression. To improve the compression rate, these models use a special module called the quantizer that improves the entropy of the generated representation. Here, we propose a novel binary quantizer and an auxiliary support module known as the remapper that co-ordinate for better compression and decompression of an image. The proposed quantizer generates the compressed binary representation whereas the remapper aids in minimizing the reconstruction error during decompression. The combined optimization of the model consisting of the encoder, quantizer, remapper, and the decoder as a single entity is not feasible due to the non-differentiable nature of the quantizer. To mitigate this, we propose a step-by-step training process wherein the encoder and decoder are trained first, then keeping the encoder parameters fixed, the remapper is trained along with the decoder to obtain a fully optimized model. The training is performed with different types of images and the results are compared to understand the best training procedures. Experimental results reveal that the proposed model can compress a 256 × 256 × 3 image to a mere 256 bits, thereby achieving a compression ratio of 6144:1; with excellent perceptual reconstruction during decompression.

Dual-stage feedback network for lightweight color image compression artifact reduction

Lossy image coding techniques usually result in various undesirable compression artifacts. Recently, deep convolutional neural networks have seen encouraging advances in compression artifact reduction. However, most of them focus on the restoration of the luma channel without considering the chroma components. Besides, most deep convolutional neural networks are hard to deploy in practical applications because of their high model complexity. In this article, we propose a dual-stage feedback network (DSFN) for lightweight color image compression artifact reduction. Specifically, we propose a novel curriculum learning strategy to drive a DSFN to reduce color image compression artifacts in a luma-to-RGB manner. In the first stage, the DSFN is dedicated to reconstructing the luma channel, whose high-level features containing rich structural information are then rerouted to the second stage by a feedback connection to guide the RGB image restoration. Furthermore, we present a novel enhanced feedback block for efficient high-level feature extraction, in which an adaptive iterative self-refinement module is carefully designed to refine the low-level features progressively, and an enhanced separable convolution is advanced to exploit multiscale image information fully. Extensive experiments show the notable advantage of our DSFN over several state-of-the-art methods in both quantitative indices and visual effects with lower model complexity.

Classification of food items in trash through images using a hybrid deep learning system

The issue that arouses concern is the issue of waste in general, especially waste in food. Deep learning has made its way into the aforementioned field. Pre-processing is done using a new filter derived from discrete wavelets that were derived from Legendre polynomials (LEGP) to achieve Multi Resolution Analyses (MRA), in which the image parameters are separated into proximity parameters and detail parameters, where the first is concentrated in LL, while the second is in HL, LH, HH to improve the input image of the waste in terms of noise removal and image compression. To begin the deep learning process, configure the New Convolutional Neural Network (NCNN) to configure 144 layer with the image size (224x224x3) with Google Net so that the waste resulting from leftover food is sorted. In this work, four foods were sorted: tomatoes, potatoes, bread, and rice, and the result was reached for accuracy for each upcoming image with the accuracy of the neural arrangement of rice (99.9337) to potato (100 %) to tomato (99.8959), and finally for bread (83.5492), with the accuracy of the neural network 94.12 % in 1 min 2 sec. The accuracy reached in this work with Discrete Legendre Wavelets Transform (DLEGWT) and training the new network for google net was 94.12 % in 1 minute and 2 seconds with the new filter (DLEGWT). A program was created that was translated into a new algorithm. It was shown that the new filter worked in two stages, which is the initial processing stage, included analyses image with removed the noise from the image, after which the image is compressed to reduce the space used by the image data. As for the second stage, the deep learning stage takes place with the new filter, so that a new convolutional neural network is created after Dividing the image into three channels RGB to complete the convolution process for each channel, so that the hidden layers are formed until it reaches full connectivity, so that the classification process is performed with triple accuracy. The proposed algorithm has proven its efficiency

Research and Implementation of FPGA Image Compression Parallel Algorithm Based on CCSDS

High-pixel, high-resolution spectral images are an important source of key graphical information obtained from space remote sensing, however, with the dramatically expanding amount of graphical information, the limited transmission bandwidth and hardware resources are difficult to meet the demand for massive data, which makes real-time image lossless compression and transmission technology an important solution. For this reason, this paper is based on the CCSDS image lossless compression algorithm, using FPGA for the construction of image compression system, and proposes a method strategy based on parallel acceleration algorithm to realize the real time processing of large-capacity hyperspectral images. The simulation results show that hyperspectral river and mountain range images can be compressed with greatly improved compression efficiency while maintaining real time performance and accuracy.

Unified and Scalable Deep Image Compression Framework for Human and Machine

Image compression aims to minimize the amount of data in image representation while maintaining a certain visual quality for humans, which is an essential technique for storage and transmission. Recently, along with the development of computer vision, machines have become another primary receiver for images and require compressed images at a certain quality level, which may be different from that of human vision. In many scenarios, compressed images should serve both human and machine vision tasks, but few compression methods are designed for both goals simultaneously. In this paper, we propose a unified and scalable deep image compression (USDIC) framework that jointly optimizes the image quality according to human and machine vision in an end-to-end style. For the encoder, we propose an information splitting mechanism (ISM) to separate images into semantic and visual features, which mainly aims at machine analysis and human viewing tasks. For the decoder, we design a scalable decoding architecture. The encoded semantic feature is first decoded for machine analysis tasks, and the image is decoded and reconstructed further by leveraging the decoded semantic features. Herein, to further remove the redundancy between the semantic and visual features of images, we propose a scalable entropy model (SEM) with a joint optimization strategy to reconstruct the image using the two kinds of decoded features. Extensive experimental results show that the proposed USDIC achieves much better performance on the image analysis task while maintaining competitive performance on the traditional image reconstruction task compared with popular image compression methods.

Reconstruction-free Image Compression for Machine Vision via Knowledge Transfer

Reconstruction-free image compression for machine vision aims to perform machine vision tasks directly on compressed-domain representations instead of reconstructed images. Existing reports have validated the feasibility of compressed-domain machine vision. However, we observe that when using recent learned compression models, the performance gap between compressed-domain and pixel-domain vision tasks is still large due to the lack of some natural inductive biases in pixel-domain convolutional neural networks. In this paper, we attempt to address this problem by transferring knowledge from pixel domain to compressed domain. A knowledge transfer loss defined at both output level and feature level is proposed to narrow the gap between compressed domain and pixel domain. In addition, we modify neural networks for pixel-domain vision tasks to better suit compressed-domain inputs. Experimental results on several machine vision tasks show that the proposed method improves the accuracy of compressed-domain vision tasks significantly, which even outperforms learning on reconstructed images while avoiding the computational cost of image reconstruction.