16 papers · ranked by Valyu relevance
Yuhang Wang, Weihua Chen, Linjing Song, Zhiping Xu + 6 more
With the rapid growth of data volume in sensor networks, lossy source coding systems achieve high-efficiency data compression with low distortion under limited transmission bandwidth. However, conventional compression algorithms rely on a two-stage framework with high computational complexity and frequently struggle to…
Neri Merhav, Chi Wan Sung
We derive a few extended versions of the Kraft inequality for information lossless finite-state encoders. The main basic contribution is in defining a notion of a Kraft matrix and in establishing the fact that a necessary condition for information losslessness of a finite-state encoder is that none of the eigenvalues…
Tao Guo, Zhangyao Song, Huihui Wu, Yang Li + 1 more
This paper analyzes the semantic rate-distortion problem motivated by task-oriented data compression with side information. The semantic information related to a task is not directly accessible to the encoder but implicitly impacts the observations through a joint probability distribution. The decoder aims to…
Neri Merhav, Jun Chen
We derive a few extended versions of the Kraft inequality for lossy compression, which pave the way to the derivation of several refinements and extensions of the well-known Shannon lower bound in a variety of instances of rate-distortion coding. These refinements and extensions include sharper bounds for one-to-one…
Christine Beavers, Herbert J. Bernstein, Aaron S. Brewster, Max Burian + 19 more
This paper is a report of the High Data Rate Macromolecular Crystallography workshop held on 23 July 2025 as part of the 2025 meeting of the American Crystallographic Association in Lombard, IL, USA, 18-23 July 2025. This report summarizes the discussions, questions, action items, and recommendations that arose from…
Alin-Adrian Alecu, Mohammad Ali Tahouri, Adrian Munteanu, Bujor Păvăloiu + 1 more
Near-lossless coding schemes traditionally rely on uniform quantization to control the maximum absolute error ( $L_{\infty}$ norm) of residual signals, often assuming a parametric model for the source distribution. This paper introduces a novel design framework for non-uniform, entropy-aware $L_{\infty}$-oriented…
Priya Stella Mary I, Rashmi Siddalingappa, Vinay M, Deepa S + 2 more
With the explosion of digital images across multiple sectors like social media, health care, medical imaging, and remote sensing, there is a demand to optimise the storage and transmission of images. In this paper, a novel Structural Fidelity Weighted Ensemble model is proposed to dynamically adjust the weights between…
Yichen Wang, Yijie Lin, Ching-Chun Chang, Chin-Chen Chang + 2 more
With the rapid advancement of the Internet of Medical Things (IoMT), the efficient transmission and management of large-scale medical images in bandwidth- and resource-constrained networks remain critical challenges. This paper proposes a high-payload data hiding method in Absolute Moment Block Truncation Coding…
Kees Schouhamer Immink, Jos H. Weber, Tuan Thanh Nguyen, Kui Cai + 2 more
The design of low-complexity and efficient constrained codes has been a major research item for many years. This paper reports on a versatile method named concatenated constrained codes for designing efficient fixed-length constrained codes with small complexity. A concatenated constrained code comprises two (or more)…
Sibusiso B. Buthelezi, Jules R. Tapamo, Nikolaos Mitianoudis
We present a hybrid end-to-end learned image compression framework that combines a CNN-based variational autoencoder (VAE) with an efficient hierarchical Swin Transformer to address the limitations of existing entropy models in capturing global dependencies under computational constraints. Traditional VAE-based codecs…
Ofer Hadar
Recent advances in image and video processing have been profoundly influenced by developments in information theory, source coding, and learning-based representations. Classical concepts such as entropy modeling, rate-distortion optimization, transform coding, and quantization are now being revisited in the context of…
Qiyu Zha, Jiangling Guo, Hocine Cherifi
Video compression is central to large-scale video delivery, where better rate-distortion efficiency directly reduces bandwidth and storage cost. A practical way to improve efficiency is to encode a low-resolution video stream with a standard codec and restore high-resolution details with a learned super-resolution…
Serhat Dikyar, Behcet Ugur Toreyin, Raimondo Schettini
The proliferation of high-definition video data necessitates highly efficient processing pipelines for real-time edge analytics. However, traditional object detection architectures rely exclusively on pixel-domain inputs, which renders the computationally prohibitive decoding phase a latency bottleneck. In this paper…
Ya Liu, Rui Zhang, Yong Zhang, Yuwei Chen + 1 more
Large field-of-view (FOV) infrared imaging, widely utilized in applications including target detection and remote sensing, generates massive datasets that pose significant challenges for transmission and storage. To address this issue, we propose an efficient lossless compression method for large FOV infrared video.…
Rafael Castelli, Tomás González, Rodrigo Torrado, Álvaro Martín + 2 more
Nanopore sequencers read DNA molecules by measuring the perturbation produced in an electric current flow, as a DNA strand passes through a nanometre-sized channel in a membrane (see [vbag157-F1]; for ease of exposition we describe DNA sequencing; RNA sequencing is analogous). This perturbation depends on the specific…
Yuansheng Wu, Liangchao Hu, Xiaodan Song, Honggang Chen
Video coding plays a critical role for efficient transmission in surveillance camera sensors. Although long-term reference (LTR) has been fully studied in traditional hand-designed video coding approaches, its potential in learned video coding is still unexplored due to the highly unequal importance between long and…