Compression Of High-resolution Images Research Articles

High-resolution image compression algorithms in remote sensing imaging

Digital image processing (DIP) has great application values in many fields, especially in remote sensing image processing, which represents the acquisition, enhancement, analysis, encoding, transmission, and storage of remote sensing images. With the development of chip technology and parallel computing technology, various digital image processing technologies have been successfully applied to satellite applications to help researchers exploit reliable information from remote-sensing images. However, the huge amount of images generated by ultra-high resolution optical remote sensing satellites put great pressure on existing transmission, storage, and processing technologies. Therefore, this paper proposes a spatio-temporal compression pipeline for remote sensing images based on lossy compression methods with ultra-high compression ratios to reduce the overhead required for the transmission and storage of remote sensing images while maintaining the quality of the compressed images. The experimental results show that the proposed method outperforms the classical image compression such as JPEG-2000.

A Multilevel Fuzzy Transform Method for High Resolution Image Compression

The Multilevel Fuzzy Transform technique (MF-tr) is a hierarchical image compression method based on Fuzzy Transform, which is successfully used to compress images and manage the information loss of the reconstructed image. Unlike other lossy image compression methods, it ensures that the quality of the reconstructed image is not lower than a prefixed threshold. However, this method is not suitable for compressing massive images due to the high processing times and memory usage. In this paper, we propose a variation of MF-tr for the compression of massive images. The image is divided into tiles, each of which is individually compressed using MF-tr; thereafter, the image is reconstructed by merging the decompressed tiles. Comparative tests performed on remote sensing images show that the proposed method provides better performance than MF-tr in terms of compression rate and CPU time. Moreover, comparison tests show that our method reconstructs the image with CPU times that are at least two times less than those obtained using the MF-tr algorithm.

YOLOv5-Based Vehicle Detection Method for High-Resolution UAV Images

To solve the feature loss caused by the compression of high-resolution images during the normalization stage, an adaptive clipping algorithm based on the You Only Look Once (YOLO) object detection algorithm is proposed for the data preprocessing and detection stage. First, a high-resolution training dataset is augmented with the adaptive clipping algorithm. Then, a new training set is generated to retain the detailed features that the object detection network needs to learn. During the network detection process, the image is detected in chunks via the adaptive clipping algorithm, and the coordinates of the detection results are merged by position mapping. Finally, the chunked detection results are collocated with the global detection results and outputted. The improved YOLO algorithm is used to conduct experiments comparing this algorithm with the original algorithm for the detection of test set vehicles. The experimental results show that compared with the original YOLO object detection algorithm, the precision of our algorithm is increased from 79.5% to 91.9%, the recall is increased from 44.2% to 82.5%, and the mAP@0.5 is increased from 47.9% to 89.6%. The application of the adaptive clipping algorithm in the vehicle detection process effectively improves the performance of the traditional object detection algorithm.

The F-transform preprocessing for JPEG strong compression of high-resolution images

We are focused on improving the JPEG compression algorithm in the cases where a high ratio compression is required. For this purpose, we propose a preprocessing based on the F-transform. We develop a new discrete version of the inverse F-transform and new hybrid image compression and decompression algorithms, which combine JPEG with the F-transform. In the compression phase, JPEG follows the direct F-transform, while in the decompression phase, the methods are performed in the opposite order, and the new inverse F-transform is applied. We justify and illustrate on high-resolution benchmarks three main advantages of the proposed combination over the pure JPEG. We performed 504600 compressions using 15 different qualities, and for each selected quality it was confirmed that the proposed approach has compression 4 times stronger and the SSIM quality is 1.6 times higher than the pure JPEG. Finally, we propose an easy way to use the new compression software in the current JPEG standard.

An Effective Algorithm and Architecture for the High-Throughput Lossless Compression of High-Resolution Images

This paper proposes a high-throughput lossless image-compression algorithm based on Golomb–Rice coding and its hardware architecture. The proposed solution increases compression ratios (CRs) while preserving the throughput by taking advantage of a novel parallel variable-length sign coding (PVSC) algorithm that reduces the sign bits to achieve a higher CR. In addition, the proposed solution adopts and modifies the two existing compression algorithms to improve the overall compression performance. The experimental results show that the proposed solution yields an average CR of 3.12, which is higher than those achieved with the previous algorithms. The hardware implementation of the proposed solution for an $8 \times 8$ block unit achieves a throughput of 18 GBps and 24 GBps when encoding and decoding, respectively. This hardware performance is enough to handle $7680\times 4320$ @240-Hz image processing.

High-Speed Fractal Image Compression Featuring Deep Data Pipelining Strategy

A new architecture based on deep data pipelining is proposed for implementing fractal compression of high-resolution images in real time. The general idea is to partition an image into overlapping range and domain blocks in which four range blocks constitute one domain block. In this way, two matching operations can be performed simultaneously using two processor units. Further reduction in the encoding time is achieved by exploiting the inherently high degree of correlation among pixels in the neighborhood areas and restricting the search in the neighboring blocks only. The design is synthesized on Altera Stratix IV field-programmable gate array and optimized at circuit level in order to achieve a high-speed implementation. The proposed architecture is evaluated in terms of the peak signal-to-noise ratio (PSNR), the runtime, the memory utilization, and the compression ratio (CR). Experimental results suggest that the proposed architecture is able to encode a 1024 × 1024 size image in 10.8 ms with PSNR and CR averaging at 27 dB and 34:1, respectively. Meanwhile, the energy dissipation is approximately 0.5 W which is comparable to the state-of-the-art fractal processors.

Implementation of the High-resolution Image Compression Algorithm based on 89s52S Single Chip Microcomputer

This paper conducts the analysis on the high-resolution image compression (IC) algorithm based on 89s52S single chip microcomputer. Image compression is an important issue in the field of image processing, at present, most algorithms, such as JPEG algorithm that compress all regions of the image with the same compression ratio. However, this kind of compression algorithm has its own inherent flaws, such as the gray discontinuity (block effect) at the boundary of each block, which will greatly affect the subjective quality of the reconstructed image. Under this basis, this paper proposes the novel IC algorithm and applies it on the hardware. The result proves the effectiveness.

Nonnegative Matrix Factorization Aided Principal Component Analysis for High-Resolution Partial Discharge Image Compression in Transformers

In this study, the development of nonnegative matrix factorization aided principal component analysis (NMF-PCA) algorithm is proposed to solve the problem that the covariance matrix cannot be computed due to the extremely high vector space caused by the “matrix-to-vector” transformation when principal component analysis (PCA) is applied to high-resolution image compression, which is further employed for PD gray image compression and recognition. In the proposed NMF-PCA algorithm, nonnegative matrix factorization (NMF) is firstly employed to decompose the high-resolution image into base matrices W and coefficient matrices H with lower dimension. Then, PCA is adopted to extract several principal components from the vectors of W and H as features. A fuzzy C-means (FCM) clustering method is responsible for PD classification and features evaluation. Using a traditional pulse current detector for PD experiment, 177 gray images associated with four PD defect types are obtained for NMF-PCA testing. The results of algorithm performance evaluation show that NMF and PCA both have fast responding time when r is less 3, which is suitable for PD on-line analysis and diagnosis. The recognition results of experimental PD samples demonstrate that only is the feature set FH extracted from the coefficient matrix H fit for PCA compression of PD gray images. Meanwhile, the maximum successful clustering rate 0.9661 is achieved by 3D features of FH with r = 2, which is much higher than 0.8023 of traditional PRPD operators. In addition, the FCM validity measures report that the features obtained by NMF-PCA have better aggregation characteristic than PRPD statistical operators. The obtained results demonstrate that the proposed NMF-PCA algorithm could provide an effective tool for PD diagnosis, and it is easy to extend to other image or matrix applications

Performance Analysis of Neural Network Architecture Combined with DWT for Image Compression

Neural networks are significantly used in signal and image processing techniques for pattern recognition and template matching. In this work neural networks are used for image compression. In order to improve the performances image compression algorithm, DWT is combined with NN for achieving better MSE and increase in compression ration greater than 100%. NN architecture achieves maximum of 98% with use of four neurons in the hidden layer, with selection of LL sub band only the compression is improved by another 75%. The proposed architecture is analyzed for 20 images and MSE is found to be improved by a factor of 20%. Daubechies wavelet filter and Haar wavelet filters are used for DWT, input layer with one hidden layer and output layer consisting of tansig and purelin function us used for compression. The design proposed is suitable for high resolution image compression. General Terms

Compression of High-Resolution Images Using DCT with Variable Block Sizes

Compression of High-Resolution Images Using DCT with Variable Block Sizes

Compression of High-Resolution Images Using DCT with Variable Block Sizes

Compression of High-Resolution Images Using DCT with Variable Block Sizes

New methods for lossless image compression using arithmetic coding

We give a new paradigm for lossless image compression, with four modular components: pixel sequence, prediction, error modeling and coding. We present two new methods (called MLP and PPPM) for lossless compression, both involving linear prediction, modeling prediction errors by estimating the variance of a Laplace distribution, and coding using arithmetic coding applied to precomputed distributions. The MLP method is both progressive and parallelizable. We give results showing that our methods perform significantly better than other currently used methods for lossless compression of high resolution images, including the proposed JPEG standard. We express our results both in terms of the compression ratio and in terms of a useful new measure of compression efficiency, which we call compression gain.