State-of-the-art in 360 video/image processing: Perception, assessment and compression
Nowadays, 360° video/image has been increasingly popular and drawn great attention. The
spherical viewing range of 360° video/image accounts for huge data, which pose the …
spherical viewing range of 360° video/image accounts for huge data, which pose the …
Deep architectures for image compression: a critical review
Deep learning architectures are now pervasive and filled almost all applications under
image processing, computer vision, and biometrics. The attractive property of feature …
image processing, computer vision, and biometrics. The attractive property of feature …
Semantic communications: Principles and challenges
Semantic communication, regarded as the breakthrough beyond the Shannon paradigm,
aims at the successful transmission of semantic information conveyed by the source rather …
aims at the successful transmission of semantic information conveyed by the source rather …
High-fidelity generative image compression
F Mentzer, GD Toderici… - Advances in Neural …, 2020 - proceedings.neurips.cc
We extensively study how to combine Generative Adversarial Networks and learned
compression to obtain a state-of-the-art generative lossy compression system. In particular …
compression to obtain a state-of-the-art generative lossy compression system. In particular …
Learned image compression with discretized gaussian mixture likelihoods and attention modules
Image compression is a fundamental research field and many well-known compression
standards have been developed for many decades. Recently, learned compression …
standards have been developed for many decades. Recently, learned compression …
Checkerboard context model for efficient learned image compression
For learned image compression, the autoregressive context model is proved effective in
improving the rate-distortion (RD) performance. Because it helps remove spatial …
improving the rate-distortion (RD) performance. Because it helps remove spatial …
Joint autoregressive and hierarchical priors for learned image compression
Recent models for learned image compression are based on autoencoders that learn
approximately invertible mappings from pixels to a quantized latent representation. The …
approximately invertible mappings from pixels to a quantized latent representation. The …
Dvc: An end-to-end deep video compression framework
Conventional video compression approaches use the predictive coding architecture and
encode the corresponding motion information and residual information. In this paper, taking …
encode the corresponding motion information and residual information. In this paper, taking …
Multi-level wavelet-CNN for image restoration
The tradeoff between receptive field size and efficiency is a crucial issue in low level vision.
Plain convolutional networks (CNNs) generally enlarge the receptive field at the expense of …
Plain convolutional networks (CNNs) generally enlarge the receptive field at the expense of …
Generative adversarial networks for extreme learned image compression
We present a learned image compression system based on GANs, operating at extremely
low bitrates. Our proposed framework combines an encoder, decoder/generator and a multi …
low bitrates. Our proposed framework combines an encoder, decoder/generator and a multi …