12 papers · ranked by Valyu relevance
Tadashi Wadayama, Kensho Nakajima, Ayano Nakai-Kasai
—The power consumption of the integrated circuit is becoming a significant burden, particularly for large-scale signal processing tasks requiring high throughput. The decoding process of LDPC codes is such a heavy signal processing task that demands power efficiency and higher decoding throughput. A promising approach…
Tadashi Wadayama, Lantian Wei
—This paper presents the Gradient Flow (GF) decoding for LDPC codes. GF decoding, a continuous-time methodology based on gradient flow, employs a potential energy function associated with bipolar codewords of LDPC codes. The decoding process of the GF decoding is concisely defined by an ordinary differential equation…
Muhammet Balcılar, Bharath Bhushan Damodaran, Karam Naser, Franck Galpin + 1 more
'Franck Galpin' 'Pierre Hellier'] End-to-end image/video codecs are getting competitive compared to traditional compression techniques that have been developed through decades of manual engineering efforts. These trainable codecs have many advantages over traditional techniques such as easy adaptation on perceptual…
Muhammet Balcılar, Bharath Bhushan Damodaran, Karam Naser, Franck Galpin + 1 more
'Franck Galpin' 'Pierre Hellier'] Abstract—End-to-end image and video codecs are becoming increasingly competitive, compared to traditional compression techniques that have been developed through decades of manual engineering efforts. These trainable codecs have many advantages over traditional techniques, such as…
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…
Shifeng Zhang, Ning Kang, Tom Ryder, Zhenguo Li
It was estimated that the world produced 59ZB (5.9 × 1013GB) of data in 2020, resulting in the enormous costs of both data storage and transmission. Fortunately, recent advances in deep generative models have spearheaded a new class of socalled "neural compression" algorithms, which significantly outperform traditional…
Pengxi Fu, Zhen Wang, Jianxin Guo, Yushuai Zhang + 4 more
Modern communication systems increasingly leverage multiple information streams-including channel observations, statistical models, and contextual knowledge-to enhance decoding reliability. However, the varying and often unpredictable quality of these sources poses a critical challenge: rigid combination rules fail…
Simone Carlo Surace, Jean-Pascal Pfister, Wulfram Gerstner, Johanni Brea + 1 more
'Johanni Brea' 'Francis Ouellette'] This is a PLOS Computational Biology Education paper. The idea that the brain functions so as to minimize certain costs pervades theoretical neuroscience. Because a cost function by itself does not predict how the brain finds its minima, additional assumptions about the optimization…
Jonathan Ho, Evan Lohn, Pieter Abbeel
Likelihood-based generative models are the backbones of lossless compression due to the guaranteed existence of codes with lengths close to negative log likelihood. However, there is no guaranteed existence of computationally efficient codes that achieve these lengths, and coding algorithms must be hand-tailored to…
Guillaume Quétant, Yury Belousov, Vitaliy Kinakh, Slava Voloshynovskiy + 2 more
'Slava Voloshynovskiy' 'Sotiris Kotsiantis' 'Marco Piangerelli'] We present a novel information-theoretic framework, termed as TURBO, designed to systematically analyse and generalise auto-encoding methods. We start by examining the principles of information bottleneck and bottleneck-based networks in the auto-encoding…
Artemy Kolchinsky, Brendan D. Tracey, David H. Wolpert
Information bottleneck (IB) is a technique for extracting information in one random variable X that is relevant for predicting another random variable Y. IB works by encoding X in a compressed “bottleneck” random variable M from which Y can be accurately decoded. However, finding the optimal bottleneck variable…
Chen-Hsiu Huang, Ja-Ling Wu, Jun Chen
End-to-end learned image compression codecs have notably emerged in recent years. These codecs have demonstrated superiority over conventional methods, showcasing remarkable flexibility and adaptability across diverse data domains while supporting new distortion losses. Despite challenges such as computational…