Zobrazeno 1 - 10
of 6 962
pro vyhledávání: '"Content adaptive"'
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 f
Externí odkaz:
http://arxiv.org/abs/2412.17464
In streaming media services, video transcoding is a common practice to alleviate bandwidth demands. Unfortunately, traditional methods employing a uniform rate factor (RF) across all videos often result in significant inefficiencies. Content-adaptive
Externí odkaz:
http://arxiv.org/abs/2411.05295
Publikováno v:
in Proceedings of the 31st ACM International Conference on Multimedia, pp. 1431-1442, 2023
Traditional image codecs emphasize signal fidelity and human perception, often at the expense of machine vision tasks. Deep learning methods have demonstrated promising coding performance by utilizing rich semantic embeddings optimized for both human
Externí odkaz:
http://arxiv.org/abs/2410.06149
Emerging holographic display technology offers unique capabilities for next-generation virtual reality systems. Current holographic near-eye displays, however, only support a small \'etendue, which results in a direct tradeoff between achievable fiel
Externí odkaz:
http://arxiv.org/abs/2409.03143
Recent advances in computer-aided diagnosis for histopathology have been largely driven by the use of deep learning models for automated image analysis. While these networks can perform on par with medical experts, their performance can be impeded by
Externí odkaz:
http://arxiv.org/abs/2409.09797
Content-adaptive compression is crucial for enhancing the adaptability of the pre-trained neural codec for various contents. Although these methods have been very practical in neural image compression (NIC), their application in neural video compress
Externí odkaz:
http://arxiv.org/abs/2405.04274
Autor:
Zhang, Yunxiang, Kuznetsov, Alexandr, Jindal, Akshay, Chen, Kenneth, Sochenov, Anton, Kaplanyan, Anton, Sun, Qi
Neural image representations have recently emerged as a promising technique for storing, streaming, and rendering visual data. Coupled with learning-based workflows, these novel representations have demonstrated remarkable visual fidelity and memory
Externí odkaz:
http://arxiv.org/abs/2407.01866
Autor:
Shi, Yunhui1 (AUTHOR) syhzm@bjut.edu.cn, Ye, Liping1 (AUTHOR) yeliping199903@163.com, Wang, Jin1 (AUTHOR) ijinwang@bjut.edu.cn, Wang, Lilong1 (AUTHOR) huh@emails.bjut.edu.cn, Hu, Hui1 (AUTHOR) ybc@bjut.edu.cn, Yin, Baocai1 (AUTHOR), Ling, Nam2 (AUTHOR) nling@scu.edu
Publikováno v:
Sensors (14248220). Aug2024, Vol. 24 Issue 16, p5439. 22p.
Currently, machine learning-based methods for remote sensing pansharpening have progressed rapidly. However, existing pansharpening methods often do not fully exploit differentiating regional information in non-local spaces, thereby limiting the effe
Externí odkaz:
http://arxiv.org/abs/2404.07543
Autor:
Menon, Vignesh V, Afzal, Samira, Rajendran, Prajit T, Schoeffmann, Klaus, Prodan, Radu, Timmerer, Christian
Adaptive live video streaming applications use a fixed predefined configuration for the bitrate ladder with constant framerate and encoding presets in a session. However, selecting optimized framerates and presets for every bitrate ladder representat
Externí odkaz:
http://arxiv.org/abs/2311.08074