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检索条件"任意字段=IEEE Data Compression Conference"

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Image compression Algorithms - Comparative Analysis 3

Image Compression Algorithms - Comparative Analysis

引用

3rd International conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Niharika Reddy, G. Namrutha, M. Akshaya, Namratha Bhaskaran, Sreebha Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331508456

Due to the abundance of the new digital media data, the issue of image quality and volume of data requiring compression has become a significant factor of concern, especially in media storage and transmitting. This work affords a comparative analysis of different image compression techniques with focus on the compression ratio, quality preservation, and complexity. A new hybrid model of Predictive Coding, Run Length Coding and Quantum Entropy Coding (QEC) is proposed and shown to exhibit negligible quality loss with substantial space saving. The experimental outcomes show that the proposed method reduces space 80 percent and works better than previous methods for areas requiring high speed and relative accuracy. These insights are timely, as practical computing-communication trade-offs are paramount in the new generation of social networks, medicine, and multimedia streaming. © 2025 ieee.

关键词： Image compression

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JPEG IMAGE CIPHERING BASED ON CHAOTIC ENCRYPTION 31

JPEG IMAGE CIPHERING BASED ON CHAOTIC ENCRYPTION

引用

2024 International conference on Image Processing

作者： Hachani, Meha Zaid, Azza Ouled Univ Tunis El Manar Natl Engn Sch Tunis SysCom Lab B.P.37Belvedere 1002 Tunis Tunisia

ISBN: (纸本)9798350349405;9798350349399

This paper proposes a chaos-based encryption method that allows conditional access to privacy-sensitive regions in Joint Photographic Experts Group (JPEG) images. Our goal is to encipher the confidential content in a way that guarantees high resistance to common attacks without severely compromising the encoding speed and bitrate cost. The encryption mechanism comprises two modules: chaotic confusion and diffusion;the first module is governed by means of nonlinear shuffling applied on the quantized AC (Alternating Current) coefficients belonging the region of interest (ROI), while the second module is ruled by means of XOR operation that encrypts the shuffled components using a cumulative one-dimensional chaotic map. The whole cryptosystem is controlled by the chaotic sequences generated by chaotic maps. Experimental results demonstrate that the proposed crypto-compression scheme is proven to be effective in balancing the trade-off between robustness and hiding ability while guaranteeing reversible recovery of the protected data.

关键词： Crypto-compression JPEG AC coefficients ROI chaotic maps

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An Improved Flash-Based Swap System for Performance and Flash Endurance

An Improved Flash-Based Swap System for Performance and Flas...

引用

ieee International conference on Consumer Electronics (ICCE)

作者： Lee, Jungseung Park, Huijin Song, Yongchun Samsung Electmo Co LTD Visual Display Div VD Suwon Gyeonggi South Korea

ISBN: (纸本)9781665491303

Recently, CE devices are increasingly supporting various features that meet user requirements, and accordingly, the demand for the amount of memory required continues to increase. compression swap technology, which stores compressed data in memory, is widely used as a solution to secure free memory, but fundamentally, compressed data occupies the memory. On the other hand, the traditional disk swap method using disk as a swap device has the advantage of not requiring memory occupancy, but in an embedded environment, it has been difficult to be widely used due to the overall system performance degradation due to disk I/O operation and the lifespan limit of flash as a disk device. In this work, we present a method to minimize the overhead caused by I/O by screening swap data to be stored in flash as a way to solve the performance and life limitations that have hindered the use of flash swap. At the same time, it shows how to asynchronously writeback the swap data and how to utilize the enhanced mode of flash to overcome the flash lifespan limit. As a result of the evaluation, our scheme shows performance degradation of less than 5% on average in the DTV user scenario compared to the existing compression swap technology in a DTV environment using total 1.5GB memory, while free memory improves by more than 130MB in idle time.

关键词： memory swap compression flash

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Autoencoded Image compression for Secure and Fast Transmission

Autoencoded Image Compression for Secure and Fast Transmissi...

引用

2024 ieee International conference on Computer Vision and Machine Intelligence, CVMI 2024

作者： Naveen, Aryan Kashyap Thunga, Sunil Murki, Anuhya Kalale, Mahati Anil, Shriya National Institute of Technology Karnataka Dept. of Information Technology Surathkal India National Institute of Technology Karnataka Dept. of Computer Science and Engineering Surathkal India

ISBN: (纸本)9798350376876

With exponential growth in the use of digital image data, the need for efficient transmission methods has become imperative. Traditional image compression techniques often sacrifice image fidelity for reduced file sizes, challenging maintaining quality and efficiency. They also compromise security, leaving images vulnerable to threats such as man-in-the-middle attacks. This paper proposes an autoencoder architecture for image compression to not only help in dimensionality reduction but also inherently encrypt the images. The paper also introduces a composite loss function that combines reconstruction loss and residual loss for improved performance. The autoencoder architecture is designed to achieve optimal dimensionality reduction and regeneration accuracy while safeguarding the compressed data during transmission or storage. Images regenerated by the autoencoder are evaluated against three key metrics: reconstruction quality, compression ratio, and one-way delay during image transfer. The experiments reveal that the proposed architecture achieves an SSIM of 97.5% over the regenerated images and an average latency reduction of 87.5%, indicating its effectiveness as a secure and efficient solution for compressed image transfer. © 2024 ieee.

关键词： Image compression

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ADAPTING LEARNED IMAGE CODECS TO SCREEN CONTENT VIA ADJUSTABLE TRANSFORMATIONS 31

ADAPTING LEARNED IMAGE CODECS TO SCREEN CONTENT VIA ADJUSTAB...

引用

2024 International conference on Image Processing

作者： Dogaroglu, H. Burak Koyuncu, A. Burakhan Boev, Atanas Alshina, Elena Steinbach, Eckehard Tech Univ Munich Sch Computat Informat & Technol Munich Germany Huawei Munich Res Ctr Munich Germany Munich Inst Robot & Machine Intelligence Munich Germany

ISBN: (纸本)9798350349405;9798350349399

As learned image codecs (LICs) become more prevalent, their low coding efficiency for out-of-distribution data becomes a bottleneck for some applications. To improve the performance of LICs for screen content (SC) images without breaking backwards compatibility, we propose to introduce parameterized and invertible linear transformations into the coding pipeline without changing the underlying baseline codec's operation flow. We design two neural networks to act as prefilters and postfilters in our setup to increase the coding efficiency and help with the recovery from coding artifacts. Our end-to-end trained solution achieves up to 10% bitrate savings on SC compression compared to the baseline LICs while introducing only 1% extra parameters.

关键词： screen content coding learned image compression prefilter postfilter deep learning

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Semantic Optimization in Lossless data compression for IoT-Driven Smart Healthcare Solutions 2

Semantic Optimization in Lossless Data Compression for IoT-D...

引用

2nd ieee International conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Manju Bargavi, S.K. Hashmi, Hina University Karnataka Bangalore India College of Computing Science and Information Technology Teerthanker Mahaveer University Uttar Pradesh Moradabad India

ISBN: (纸本)9798350369960

In this study, we explore the potential of the Internet of Things (IoT) within the domain of smart healthcare management applications, an area of growing importance in Computer Science, Artificial Intelligence (AI), Machine Learning (ML), and data Mining. IoT's capacity to efficiently amass, process, and transfer data is revolutionizing smart city initiatives, healthcare, energy distribution, and vehicular technology. Our primary aim is to construct a robust framework for smart healthcare management that not only harnesses the power of IoT but also integrates ontological models for enhanced data compression without compromising data quality. Our proposed theoretical framework is structured around three integral components: the pre-processing module, the ontological module, and the data compression module. Leveraging a comprehensive dataset from a Kaggle repository, we applied this tripartite approach to smart healthcare data, seeking to refine the data transformation process into a digital format - a process traditionally fraught with challenges due to the voluminous nature of healthcare data. We introduce a novel compression method and evaluate its performance against traditional compression algorithms such as Lempel-Ziv-Welch (LZW), Run-Length Encoding (RLE), and Huffman coding. The efficacy of our approach is measured using metrics like compression factor (CF), compression ratio (CR), and compression time, providing a quantitative assessment of its viability. The integration of IoT into the ontological approaches and data compression techniques presented in this study not only enhances healthcare services, but also highlights the potential of AI and ML to transform data processing and data storage solutions. This study seeks to demonstrate the benefits of this multi-functional approach and its surprising results in the future of smart healthcare. © 2023 ieee.

关键词： Efficient data Storage for Medical IoT Systems IoT in Healthcare Lossless compression Algorithms Semantic data compression Smart Healthcare data Management

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LEARNING MULTI-RATE VECTOR QUANTIZATION FOR REMOTE DEEP INFERENCE

LEARNING MULTI-RATE VECTOR QUANTIZATION FOR REMOTE DEEP INFE...

引用

ieee International conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Malka, May Ginzach, Shai Shlezinger, Nir Ben Gurion Univ Negev Sch ECE Beer Sheva Israel Rafael Adv Def Syst Haifa Israel

ISBN: (纸本)9798350302615

Remote inference accommodates a broad range of scenarios, where inference is carried out using data acquired at a remote user. When the sensing and inferring users communicate over rate limited channels, compression of the data reduces latency, and deep learning enables to jointly learn the compression encoding along with the inference rule. However, because the data is compressed into a fixed number of bits, the resolution cannot be adapted to changes in channel conditions. In this work we propose a multi-rate remote deep inference scheme, which trains a single encoder-decoder model that uses learned vector quantizers while supporting different quantization levels. Our scheme is based on designing nested codebooks along with a learning algorithm based on progressive learning. Numerical results demonstrate that the proposed scheme yields remote deep inference that operates with multiple rates while approaching the performance of fixed-rate models.

关键词： Remote inference adaptive compression

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DEEP NEURAL NETWORK MODEL compression AND SIGNAL PROCESSING 49

DEEP NEURAL NETWORK MODEL COMPRESSION AND SIGNAL PROCESSING

引用

49th ieee International conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ukil, Arijit Majumdar, Angshul Jara, Antonio J. Gama, Joao Tata Consultancy Serv Bengaluru India Indraprastha Inst Informat Technol Delhi India Libelium Zaragoza Spain Univ Porto Porto Portugal

ISBN: (纸本)9798350374520;9798350374513

Deep neural networks (DNN) are used to analyze images, videos, signals and texts require a lot of memory and intensive computing power. For example, the very successful GPT4 model contains more than a few trillion parameters. Although such models are of great impact, but they have been used very little in real-world applications, including industrial Internet of Things, self-driving cars, algorithmic health monitoring for use in limited mobile or edge devices. The requirement to run large models on resource-constrained peripherals has led to significant research interest in compressing DNN models. Signal processing researchers have traditionally advocated data (image/video/audio) compression, and by the way, many of these techniques are used for DNN compression. For example, source coding is a basic technique that has been widely used to compress various DNN models. In this paper, we present our views on the use of signal processing methods for DNN model compression.

关键词： Deep neural network signal processing model compression quantization pruning

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Exploiting Differential-Based data Encoding for Enhanced Query Efficiency 25

Exploiting Differential-Based Data Encoding for Enhanced Que...

引用

30th Asia and South Pacific Design Automation conference, ASP-DAC 2025

作者： Liu, Fangxin Wang, Zongwu Xu, Peng Huang, Shiyuan Jiang, Li Department of Computer Science and Engineering Shanghai Jiao Tong University China Shanghai Qi Zhi Institute China MoE Key Lab of Artificial Intelligence Ai Institute Shanghai Jiao Tong University China

ISBN: (纸本)9798400706356

Storing large-scale high-dimensional data, which is rapidly generated by both industry and academia, poses substantial challenges, primarily in terms of storage and maintenance costs. While data compression techniques offer a potential solution to these challenges, they must overcome two critical hurdles: 1) preserving data integrity within lossless bounds and 2) maintaining query performance on compressed *** address the above challenges, we propose a novel approach based on differential techniques that combine high-dimensional data compression with an efficient query engine. Specifically, Our methodology begins by employing vector quantization to transform high-dimensional vectors into compact integer sequences, known as quantization codes - -a classical and versatile compression method. Subsequently, we introduce DCQ, which builds upon the differential concept to perform lossless compression on quantization codes. DCQ organizes quantization codes into a tree structure and constructs a hierarchy of trees by analyzing the dissimilarity between two quantization codes. To minimize the impact on query performance, we have developed an adaptive reconstruction algorithm that partitions and reconstructs the original tree structure. This algorithm effectively balances the workload for searching each sub-tree, optimizing the compression-performance trade-off. Finally, we adopt a storage format based on multiple subtrees for rapid searching, thereby enabling differential storage to facilitate efficient queries. Extensive experiments on various large-scale real-world datasets show that DCQ achieves a compression ratio of up to 2.5 on the quantization codes and achieves > 50× performance improvement compared to the state-of-the-art general-purpose lossless compression techniques. © 2025 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Trees (mathematics)

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On the Method of Lossless data compression Using Spans of Varied Bit Widths 47

On the Method of Lossless Data Compression Using Spans of Va...

引用

47th International conference on Telecommunications and Signal Processing, TSP 2024

作者： Samofalov, Andrii Polak, Ladislav Dhaka, Arvind Brno University of Technology Feec Department of Radio Electronics Technicka 12 Brno616 00 Czech Republic Manipal University Department of Computer and Communication Engineering Rajasthan Jaipur India

ISBN: (纸本)9798350365597

Many contemporary lossless data compression methods integrate run-length encoding, along with variations of Huffman coding and the Lempel-Ziv algorithm. It has been established that no lossless data compression algorithm can effectively reduce the length of any file. Consequently, specific compression techniques may yield better results for certain types of input data, while others may be more suitable for different scenarios. This paper outlines the most common features and techniques utilized in lossless data compression methods that employ spans of varying bit widths. The implementation of span compression, which utilizes fixed-width headers to encode information about span properties, was compared with variations of Huffman encoding and the Lempel-Ziv-Welch algorithm using files from the Canterbury Corpus. The obtained results indicate the potential of span compression. © 2024 ieee.

关键词： Image compression

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