Results 201 to 210 of about 146,122 (232)

Lossless Image Compression Using a Multi-scale Progressive Statistical Model

Asian Conference on Computer Vision, 2021
Lossless image compression is an important technique for image storage and transmission when information loss is not allowed. With the fast development of deep learning techniques, deep neural networks have been used in this field to achieve a higher ...
Honglei Zhang, Francesco Cricri, H. R. Tavakoli, Nannan Zou, Emre B. Aksu, M. Hannuksela   +5 more
semanticscholar   +1 more source

A Visually Meaningful Image Encryption Scheme Based on Lossless Compression SPIHT Coding

IEEE Transactions on Services Computing, 2023
With the popularity of social networks and the increase of cloud platform applications, service computing has also developed. Therefore, the protection of information even privacy uploaded to the cloud server has become critical.
Yang Yang, Ming-Shing Cheng, Yingqiu Ding, Weiming Zhang   +3 more
semanticscholar   +1 more source

The CCSDS 123.0-B-2 “Low-Complexity Lossless and Near-Lossless Multispectral and Hyperspectral Image Compression” Standard: A comprehensive review

IEEE Geoscience and Remote Sensing Magazine, 2021
The Consultative Committee for Space Data Systems (CCSDS) published the CCSDS 123.0-B-2, “Low-Complexity Lossless and Near-Lossless Multispectral and Hyperspectral Image Compression” standard.
Miguel Hernández-Cabronero, A. Kiely, M. Klimesh, Ian Blanes, Jonathan G. Ligo, E. Magli, J. Serra-Sagristá   +6 more
semanticscholar   +1 more source

Large Language Model for Lossless Image Compression with Visual Prompts

arXiv.org
Recent advancements in deep learning have driven significant progress in lossless image compression. With the emergence of Large Language Models (LLMs), preliminary attempts have been made to leverage the extensive prior knowledge embedded in these ...
Junhao Du, Chuqin Zhou, Ning Cao, Gang Chen, Yunuo Chen, Zhengxue Cheng, Li Song, Guo Lu, Wenjun Zhang   +8 more
semanticscholar   +1 more source

CALLIC: Content Adaptive Learning for Lossless Image Compression

AAAI Conference on Artificial Intelligence
Learned lossless image compression has achieved significant advancements in recent years. However, existing methods often rely on training amortized generative models on massive datasets, resulting in sub-optimal probability distribution estimation for ...
Daxin Li, Yuanchao Bai, Kai Wang, Junjun Jiang, Xianming Liu, Wen Gao   +5 more
semanticscholar   +1 more source

Bilateral Context Modeling for Residual Coding in Lossless 3D Medical Image Compression

IEEE Transactions on Image Processing
Residual coding has gained prevalence in lossless compression, where a lossy layer is initially employed and the reconstruction errors (i.e., residues) are then losslessly compressed.
Xiangrui Liu, Meng Wang, Shiqi Wang, S. Kwong   +3 more
semanticscholar   +1 more source

High-Throughput Lossy-to-Lossless 3D Image Compression

IEEE Transactions on Medical Imaging, 2020
The rapid increase in medical and biomedical image acquisition rates has opened up new avenues for image analysis, but has also introduced formidable challenges.
D. Rossinelli, G. Fourestey, F. Schmidt, B. Busse, V. Kurtcuoglu   +4 more
semanticscholar   +1 more source

The development of a clinically tested visually lossless Image compression system for capsule endoscopy

Signal processing. Image communication, 2019
Background and objective: Capsule endoscopy investigations currently capture a large number of images that must be processed and transferred inside the capsule.
Qasim Al-shebani, P. Premaratne, P. Vial, Darryl J. McAndrew   +3 more
semanticscholar   +1 more source

Deep Lossy Plus Residual Coding for Lossless and Near-Lossless Image Compression

IEEE Transactions on Pattern Analysis and Machine Intelligence
Lossless and near-lossless image compression is of paramount importance to professional users in many technical fields, such as medicine, remote sensing, precision engineering and scientific research.
Yuanchao Bai, Xianming Liu, Xiangyang Ji   +2 more
exaly   +2 more sources

Residual‐error prediction based on deep learning for lossless image compression

Electronics Letters, 2018
A novel residual-error prediction method based on deep learning with application in lossless image compression is introduced. The proposed method employs machine learning tools to minimise the residual error of the employed prediction tools. Experimental
I. Schiopu, A. Munteanu
semanticscholar   +1 more source