no code implementations • 24 Jun 2024 • Ivan Kirillov, Denis Parkhomenko, Kirill Chernyshev, Alexander Pletnev, Yibo Shi, Kai Lin, Dmitry Babin
Learned video compression methods already outperform VVC in the low-delay (LD) case, but the random-access (RA) scenario remains challenging.
no code implementations • 7 Oct 2023 • Meng Li, Yibo Shi, Jing Wang, Yunqi Huang
With the growing demand for video applications, many advanced learned video compression methods have been developed, outperforming traditional methods in terms of objective quality metrics such as PSNR.
no code implementations • 29 Jul 2022 • Yibo Shi, Yunying Ge, Jing Wang, Jue Mao
With these powerful techniques, this paper proposes AlphaVC, a high-performance and efficient learned video compression scheme.
no code implementations • 28 Jul 2022 • Meng Li, Shangyin Gao, Yihui Feng, Yibo Shi, Jing Wang
In recent years, with the development of deep neural networks, end-to-end optimized image compression has made significant progress and exceeded the classic methods in terms of rate-distortion performance.
no code implementations • ICLR Workshop Neural_Compression 2021 • Yunying Ge, Jing Wang, Yibo Shi, Shangyin Gao
In learning-based image compression approaches, compression models are based on variational autoencoder(VAE) framework and optimized by a rate-distortion objective function, which achieve better performance than hybrid codecs.