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检索条件"任意字段=International Conference on Data Compression, Communications and Processing"

共 25987 条记录，以下是81-90 订阅

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Image data Hiding in Neural Compressed Latent Representations

Image Data Hiding in Neural Compressed Latent Representation...

引用

2023 IEEE international conference on Visual communications and Image processing, VCIP 2023

作者： Huang, Chen-Hsiu Wu, Ja-Ling National Taiwan University Dept. of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350359855

We propose an end-To-end learned image data hiding framework that embeds and extracts secrets in the latent representations of a generic neural compressor. By leveraging a perceptual loss function in conjunction with our proposed message encoder and decoder, our approach simultaneously achieves high image quality and high bit accuracy. Compared to existing techniques, our framework offers superior image secrecy and competitive watermarking robustness in the compressed domain while accelerating the embedding speed by over 50 times. These results demonstrate the potential of combining data hiding techniques and neural compression and offer new insights into developing neural compression techniques and their applications. © 2023 IEEE.

关键词： Steganography

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LOCALIZATION OF IMAGE SPLICING UNDER SEGMENT ANYTHING MODEL WITH INTEGRATED compression AND EDGE ARTIFACTS 31

LOCALIZATION OF IMAGE SPLICING UNDER SEGMENT ANYTHING MODEL ...

引用

2024 international conference on Image processing

作者： Zhao, Ruhao Zhong, Xian Liao, Liang Liu, Wenxuan Huang, Wenxin Wang, Zheng Wuhan Univ Technol Hubei Key Lab Transportat Internet Things Sch Comp Sci & Artificial Intelligence Wuhan Peoples R China Hubei Univ Sch Comp Sci & Informat Engn Wuhan Peoples R China Nanyang Technol Univ Rapid Rich Object Search Lab Sch Elect & Elect Engn Singapore Singapore Nanyang Technol Univ Coll Comp & Data Sci Singapore Singapore Wuhan Univ Natl Engn Res Ctr Multimedia Software Wuhan Peoples R China

ISBN: (纸本)9798350349405;9798350349399

The localization of image splicing involves identifying pixels in an image that have been spliced from other images, necessitating the discernment of splicing features. Despite significant advancements driven by the rise of social media and deep learning, existing methods exhibit limitations, often neglecting the integration of coarse and precise features and lacking the ability to understand objects. This leads to erroneous predictions in identifying spliced regions. This paper proposes Segment Anything Model with Integrated compression and Edge artifacts (SAM-ICE) for the localization of image splicing, addressing these limitations by fusing forged edge features and compression artifact features. Leveraging SAM's object understanding ability, our method identifies spliced regions using the fused features as guidance. Specifically, we employ Edge Artifact Extractor (EAE) to extract fine high-frequency edge splicing features and compression Artifact Extractor (CAE) to extract coarse compression artifact features. By combining these features, our method utilizes coarse-fine features to accurately pinpoint the spliced portions of the image. Experimental results demonstrate the superior accuracy, robustness, and generalizability of our method compared to the state-of-the-arts.

关键词： Image Splicing Forgery Localization Coarse-fine Feature Fusion Edge Splicing Features compression Artifacts

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Early Determination for Intra Block Copy Prediction with Refined Screen Content Detection

Early Determination for Intra Block Copy Prediction with Ref...

引用

2024 conference on Visual communications and Image processing

作者： Sun, Zhenhao Wang, Meng Zhang, Yingwen Wang, Shiqi Kwong, Sam City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Lingnan Univ Sch Data Sci Hong Kong Peoples R China

ISBN: (纸本)9798331529543;9798331529550

The intra block copy (IBC) mode introduced can significantly improve the coding efficiency for screen content. However, with the mixed content, the coding performance improvement of IBC is limited and while also introducing the increases of the coding complexity. This paper proposes an early determination scheme for IBC prediction to reduce the encoding complexity over mixed content sequences containing captured content regions. Our method is based on a refined CTU-level screen content detection method, which can better distinguish screen content in mixed scenes. Experimental results show that the proposed method reduces encoding complexity with moderate coding performance loss on mixed content sequences.

关键词： H.266/VVC screen content intra block copy encoder optimization text detection

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Wavelet-Based data compression for Remote Sensing and Image processing

Wavelet-Based Data Compression for Remote Sensing and Image ...

引用

2024 international conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Yadav, Dhyan Chandra Dadhich, Atul Saha, Ananya Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Electrical Engineering Jaipur India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

Wavelet-based totally records compression is a form of information compression used to method far flung sensing and picture processing. This technique makes use of wavelets, that are mathematical features that divide a signal into separate frequency components, and gift the signal as a sum of the components. Wavelet-primarily based compression permits for compression of statistics into a smaller, extra efficient form without sizeable lack of best. furthermore, it could additionally produce precise reconstructions of the original sign or image, making it a feasible tool for remote sensing and image processing. This approach is utilized in a selection of applications including medical imaging, television transmission, satellite tv for pc imagery and far flung sensing. via utilising wavelets, more efficient and special records may be received. moreover, due to its low time complexity, wavelet-based compression is ideal for processing big quantities of data speedy and efficaciously. © 2024 IEEE.

关键词： Best compression data Ejficaciously Lack processing Television Time

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Reversible data Hiding Scheme Based on Prediction Error Bit-plane compression 21

Reversible Data Hiding Scheme Based on Prediction Error Bit-...

引用

21st IEEE international Symposium on Parallel and Distributed processing with Applications, 13th IEEE international conference on Big data and Cloud Computing, 16th IEEE international conference on Social Computing and Networking and 13th international conference on Sustainable Computing and communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Yuan, Guo Heng, Chao Ping, Zhai Ziqi, Liu Haibo, Li Wenpeng, Li Heilongjiang University College of Computer Science and Technology Harbin China Qiqihar University College of Computer and Control Engineering Qiqihar China

ISBN: (纸本)9798350329223

How to maximize embedding capacity is one of the current challenges in the field of reversible data hiding. A reversible data hiding scheme is proposed based on the rearrangement and compression of prediction error bit-planes in this paper. The image holder first predicts the pixels to obtain the error map, then decomposes them into bit-planes, rearranges and compresses them to maximize the embedding space, and embeds the auxiliary information and secret information into the freed space according to certain rules to generate a cryptographic image. After acquiring the cryptographic image, the image receiver extracts the embedded secret information and recovers the original image using the auxiliary information. In this paper, a rearrangement strategy based on full pixel correlation was built to maximize compression efficiency and further compress the auxiliary information to free up more embeddable space and improve embedding capacity. A large number of test has revealed that the embedding rate of this method is 30% higher on average than the current state-of-the-art algorithm. © 2023 IEEE.

关键词： Pixels

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Lookup Register-Tables with Interpolation for Effective Image Transformation on x86/64 CPUs

Lookup Register-Tables with Interpolation for Effective Imag...

引用

2024 conference on Visual communications and Image processing

作者： Kamei, Hirokazu Honda, Soichiro Hayashi, Kohei Maeda, Yoshihiro Fukushima, Norishige Nagoya Inst Technol Grad Sch Engn Nagoya Aichi Japan Shibaura Inst Technol Coll Engn Tokyo Japan

ISBN: (纸本)9798331529543;9798331529550

Lookup tables (LUTs) are commonly used to speed up image processing by handling complex mathematical functions like sine and exponential calculations. They are used in various applications such as camera image processing, high-dynamic range imaging, and edge-preserving filtering. However, due to the increasing gap between computing and input/output performance, LUTs are becoming less effective. Even though specific circuits like SIMD can improve LUT efficiency, they still need to bridge the performance gap fully. The gap makes it difficult to choose between direct numerical and LUT calculations. For this problem, a register-LUTs method with the nearest neighbor was proposed;however, it is limited for functions with narrow-range values approaching zero. In this paper, we propose a method for using register LUTs to process images efficiently over a wide range of values. Our contributions include proposing register-LUT with linear interpolation for efficient computation, using a smaller data type for further efficiency, and suggesting an efficient data retrieving method.

关键词： SIMD register-LUT interpolation x86/64 CPU image processing

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DEEP NEURAL NETWORK MODEL compression AND SIGNAL processing 49

DEEP NEURAL NETWORK MODEL COMPRESSION AND SIGNAL PROCESSING

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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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LOW-COMPLEXITY VECTOR SOURCE CODING FOR DISCRETE LONG SEQUENCES WITH UNKNOWN DISTRIBUTIONS 49

LOW-COMPLEXITY VECTOR SOURCE CODING FOR DISCRETE LONG SEQUEN...

引用

49th IEEE international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Woldemariam, Leah Liu, Hang Scaglione, Anna Cornell Univ Dept Elect & Comp Engn Cornell Tech Ithaca NY 14850 USA

ISBN: (纸本)9798350344868;9798350344851

In this paper, we propose a source coding scheme that represents data from unknown distributions through frequency and support information. Existing encoding schemes often compress data by sacrificing computational efficiency or by assuming the data follows a known distribution. We take advantage of the structure that arises within the spatial representation and utilize it to encode run-lengths within this representation using Golomb coding. Through theoretical analysis, we show that our scheme yields an overall bit rate that nears entropy without a computationally complex encoding algorithm and verify these results through numerical experiments.

关键词： data compression run-length coding vector coding bit analysis

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Compressive Method for Securing Text data using Base64 Encoding and Fernet Cryptography 2

Compressive Method for Securing Text Data using Base64 Encod...

引用

2nd international conference on Sustainable Computing and Smart Systems (ICSCSS)

作者： Zala, Dhruvi Karansinh Khan, Mohammad Akram Madhav Univ Fac Engn & Technol Pindwara 307026 Rajasthan India

ISBN: (纸本)9798350391558;9798350379990

This research study introduces a compressive method for securing Hindi text data, utilizing a combination of Base64 encoding and Fernet cryptography followed by ZIP compression. Initially, the Hindi text undergoes transformation into plain text format through Base64 encoding, ensuring compatibility with subsequent encryption processes. The encoded text is then subjected to encryption using Fernet cryptography, a highly secure symmetric encryption technique known for its robust protection and data integrity capabilities. Subsequently, the encrypted text is compressed using ZIP compression, aimed at enhancing storage efficiency and facilitating faster transmission speeds. The resultant cipher data, incorporating both encryption and compression, offers a secure and optimized solution for storing and transmitting sensitive Hindi text data. This method effectively safeguards confidentiality, integrity, and efficiency in managing digital communications, addressing the critical need for secure handling of textual information in today's data-driven environments.

关键词： Base64 encoding Fernet cryptography Hindi text security data compression symmetric encryption

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Preprocessing of Pure Tone Audiometry data and Design of Machine Learning Models for Hearing Loss Classification 8

Preprocessing of Pure Tone Audiometry Data and Design of Mac...

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8th international conference on Imaging, Signal processing and communications (ICISPC)

作者： Shin, Jaesung Ma, Jun Choi, Seong Jun Hong, Min Soonchunhyang Univ Dept Software Convergence Asan South Korea Soonchunhyang Univ Cheonan Hosp Coll Med Dept Otorhinolaryngol Head & Neck Surg Asan South Korea Soonchunhyang Univ Dept Comp Software Engn Asan South Korea

ISBN: (纸本)9798350367164;9798350367157

In this research, we conducted a preprocessing step on pure-tone audiometry image data to determine the presence or absence of hearing loss in patients, and designed machine learning models for hearing loss classification using the preprocessed data. The dataset utilized consisted of a total of 18,530 data (9,265 normal, 9,265 hearing loss), and detailed parameter configuration was performed using randomly extracted training and validation data. This research involved converting image data into a CSV file format during the preprocessing stage, and the preprocessed data was then utilized to design and construct the Logistic Regression and Decision Tree Classifier machine learning models for patient classification. These models achieved an accuracy of 85.61% and 85.25%, respectively, in automatically determining the presence or absence of hearing loss.

关键词： machine learning Hearing loss Logistic Regression Decision Tree Classifier Air Conduction (AC) Bone Conduction Audiometry (BC) Pure Tone Audiometry (PTA) Binary classification model

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