Digital Imaging and Communications in Medicine (DICOM) is a widely used standard for handling, storing, and sharing medical images. However, the large file sizes associated with DICOM data pose challenges for storage and data transfer. Data reduction helps mitigate these challenges by reducing the size of the data while maintaining its integrity. This paper examines various compression methods to reduce the size of DICOM files. We evaluate 5 lossless and 4 lossy compressors on DICOM data. This study aims to compare and evaluate the performance of these compressors. By analyzing each compressor’s compression efficiency and produced image fidelity, this research seeks to determine the most effective compression strategy. Results show SZ3 is able to achieve 183.74× with error bound 1e−7and ZFP received compression bandwidth 303.82 MB/s while error bound is 1e−7.

The explosive data growth in high-performance computing (HPC) puts pressure on systems to process huge amounts of data and poses threats to the security of important data during transmission. Traditional data protection methods such as encryption inevitably attracts intermediate intercepting entities' attention. As a means of hiding information within other irrelevant data (carrier), steganography is used to transmit critical data without arousing the attention of regulators. Error-bounded lossy compression is a data reduction technique that effectively alleviates system pressures due to large data volumes. In this paper, we propose a steganography scheme based on the lossy compressor SZ named StegaZ. StegaZ performs steganography while compressing data by selecting random bits for insertion based on the password entered by the user. StegaZ does not affect unaware users' normal usage of the decompressed dataset. The experimental results show StegaZ preserves more than 99.6% of the original compression ratio and achieves a PSNR of 100% when selecting an appropriate dataset. Additionally, it imposes minimal compression bandwidth overhead, sometimes even able to obtain a higher compression bandwidth than the original.

Autonomous vehicles greatly rely on their perception system for navigation. Semantic segmentation provides a much better understanding of a vehicle’s surroundings than object detection. Unfortunately, complete image segmentation comes at a higher computational cost than object detection, which complicates developing a real-time perception system using semantic segmentation. Perception systems contain other bottlenecks too, and are not only limited by their deep learning model. An inherent amount of latency exists in data transfer, specifically through Ethernet. A vehicle’s camera feed must be transferred to an edge device for image processing as part of the autonomous driving decision-making process. This study investigates decreasing image transfer time by using various levels of JPEG compression as well as further understanding how compression affects the accuracy of semantic segmentation. Additionally, as most autonomous driving research focuses on urban environments, we look to explore autonomous unmanned ground vehicles (UGVs) in the off-road space by using the Rellis-3D dataset. We train and evaluate SwiftNet, a state-of-the-art semantic segmentation model, at different JPEG compression ratios and identify the accuracy. The transfer time of these different compression ratios is tested on three images. Results show a continual decrease in accuracy occurs as the compression ratios increase. When training SwiftNet on the train set with no compression, the highest compression ratio of 16.96 achieves a mean intersection over union (mIoU) score of 67.9% compared to the baseline achieving 78.9% mIoU. There is an increase in the accuracy of the higher compression ratios by training SwiftNet on the corresponding compression ratios; the highest compression ratio reaches 74.9% mIoU. Lastly, we notice a positive transfer speedup of these higher compression ratios when inducing JPEG compression in all transfer scenarios: (a) 1870 images (b) 10 images, (c) 1 image. Each scenario has a speedup of 1.18×, 1.14×, and 1.06×, respectively.

As high-performance computing systems rapidly advance, the volumes of scientific data produced are a prominent issue. Data reduction methods, including compression and sampling, seek to alleviate these bottlenecks by significantly reducing overall size. Yet, research shows sampling yields lower overall reconstruction quality than lossy compression due to its inability to bound the error it introduces. This work presents and assesses the ability to estimate reconstruction error. We propose an absolute error estimation scheme using various metrics and evaluate its capabilities over several real-world datasets. Upon evaluation, our scheme is between 63.6% and 94.9% accurate at estimating absolute error and has an average 87.7% correlation with the error. Lastly, we implement and discuss applications of our error estimation, including a novel sampler enhancement that allows for user-specified targeted data quality and a process to achieve a user-specified storage constraint while yielding a better preservation of data than existing methods.

Due to the increasing scale of scientific research, scientists need to collect massive amounts of data to solve complex scientific problems. The exponential growth of data poses significant challenges to high-performance computing (HPC) systems in terms of their computational ability, storage capacity, and transmission bandwidth. Data reduction techniques such as data compression have become one of the most promising solutions to these problems. Error-bounded lossy compression is now commonly utilized in HPC systems to substantially reduce data volume while precisely maintaining data accuracy. However, the majority of research was done on improving compression efficiency, such as compression ratio, and insufficient attention is paid to the security of the compression process.In this paper, we concentrate on the impact of corruption on error-bounded lossy compressor SZ, including corruption due to transient failures of hardware and corruption injected by malicious users. We analyze and quantify the influence of this corruption on compressed datasets by simulating the corruption errors that occur in the regression coefficient values and computation during compression using four failure models. The results demonstrate that SZ’s prediction-based design makes it sensitive to corruption of the regression coefficients. A single bit-flip in the regression coefficients can result in noticeable error propagation, in some cases, the compression ratio fluctuates up to 0.28%, but peak signal-to-noise ratio(PSNR) drops to negative levels.

Due to I/O bandwidth limitations, intelligent in situ data reduction methods are needed to enable post-hoc workflows. Current state-of-the-art sampling methods save data points if they deem them spatially or temporally important. By analyzing the properties of the data values at each time-step, two consecutive steps may be very similar. This research follows the notion that if neighboring time-steps are very similar, samples from both are unnecessary, which leaves storage for adding more useful samples. Here, we present an investigation of the combination of spatial and temporal sampling to drastically reduce data size without the loss of valuable information. We demonstrate that, by reusing samples, our reconstructed data set reduces the overall data size while achieving a higher post-reconstruction quality over other reduction methods.