Eirikur Agustsson
Eirikur Agustsson is a Senior Research Scientist at Google Research in Zurich. He has mainly worked on (generative) image and video compression using neural networks, image-super-resolution and generative adversarial networks. He obtained a PhD & MSc degree in Electrical Engineering from ETH Zurich and B.Sc in Mathematics & B.Sc in Electrical Engineering from the University of Iceland.
A full overview of publications can be found on the Google Scholar page
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Neural Video Compression using GANs for Detail Synthesis and Propagation
European Conference on Computer Vision (2022)
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We present the first neural video compression method based on generative adversarial networks (GANs). Our approach significantly outperforms previous neural and non-neural video compression methods in a user study, setting a new state-of-the-art in visual quality for neural methods. We show that the GAN loss is crucial to obtain this high visual quality. Two components make the GAN loss effective: we i) synthesize detail by conditioning the generator on a latent extracted from the warped previous reconstruction to then ii) propagate this detail with high-quality flow. We find that user studies are required to compare methods, i.e., none of our quantitative metrics were able to predict all studies. We present the network design choices in detail, and ablate them with user studies.
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VCT: A Video Compression Transformer
Sung Jin Hwang
NeurIPS 2022, NeurIPS 2022
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We show how transformers can be used to vastly simplify neural video compression. Previous methods have been relying on an increasing number of architectural biases and priors, including motion prediction and warping operations, resulting in complex models. Instead, we independently map input frames to representations and use a transformer to model their dependencies, letting it predict the distribution of future representations given the past. The resulting video compression transformer outperforms previous methods on standard video compression data sets. Experiments on synthetic data show that our model learns to handle complex motion patterns such as panning, blurring and fading purely from data. Our approach is easy to implement, and we release code to facilitate future research.
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Nonlinear Transform Coding
Philip A. Chou
Sung Jin Hwang
IEEE Trans. on Special Topics in Signal Processing, 15 (2021) (to appear)
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We review a class of methods that can be collected under the name nonlinear transform coding (NTC), which over the past few years have become competitive with the best linear transform codecs for images, and have superseded them in terms of rate–distortion performance under established perceptual quality metrics such as MS-SSIM. We assess the empirical rate–distortion performance of NTC with the help of simple example sources, for which the optimal performance of a vector quantizer is easier to estimate than with natural data sources. To this end, we introduce a novel variant of entropy-constrained vector quantization. We provide an analysis of various forms of stochastic optimization techniques for NTC models; review architectures of transforms based on artificial neural networks, as well as learned entropy models; and provide a direct comparison of a number of methods to parameterize the rate–distortion trade-off of nonlinear transforms, introducing a simplified one.
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High Fidelity Generative Image Compression
Michael Tschannen
Advances in Neural Information Processing Systems 34 (2020)
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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, we investigate normalization layers, generator and discriminator architectures, training strategies, as well as perceptual losses. In contrast to previous work, i) we obtain visually pleasing reconstructions that are perceptually similar to the input, ii) we operate in a broad range of bitrates, and iii) our approach can be applied to high-resolution images. We bridge the gap between rate-distortion-perception theory and practice by evaluating our approach both quantitatively with various perceptual metrics, and with a user study. The study shows that our method is preferred to previous approaches even if they use more than 2x the bitrate.
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Scale-Space Flow for End-to-End Optimized Video Compression
Sung Jin Hwang
2020 IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR)
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Despite considerable progress on end-to-end optimized deep networks for image
compression, video coding remains a challenging task. Recently proposed
methods for learned video compression use optical flow and bilinear warping
for motion compensation and show competitive rate-distortion performance
relative to hand-engineered codecs like H.264 and HEVC. However, these
learning-based methods rely on complex architectures and training schemes
including the use of pre-trained optical flow networks, sequential training of
sub-networks, adaptive rate control, and buffering intermediate
reconstructions to disk during training. In this paper, we show that a
generalized warping operator that better handles common failure cases,
e.g. disocclusions and fast motion, can provide competitive compression
results with a greatly simplified model and training procedure. Specifically,
we propose scale-space flow, an intuitive generalization of optical
flow that adds a scale parameter to allow the network to better model
uncertainty. Our experiments show that a low-latency video compression model
(no B-frames) using scale-space flow for motion compensation can outperform
analogous state-of-the art learned video compression models while being
trained using a much simpler procedure and without any pre-trained optical
flow networks.
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A popular approach to learning encoders for lossy compression is to use additive uniform noise during training as a differentiable approximation to test-time quantization. We demonstrate that a uniform noise channel can also be implemented at test time using universal quantization (Ziv, 1985). This allows us to eliminate the mismatch between training and test phases while maintaining a completely differentiable loss function. Implementing the uniform noise channel is a special case of the more general problem of communicating a sample, which we prove is computationally hard if we do not make assumptions about its distribution. However, the uniform special case is efficient as well as easy to implement and thus of great interest from a practical point of view. Finally, we show that quantization can be obtained as a limiting case of a soft quantizer applied to the uniform noise channel, bridging compression with and without quantization.
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We address interactive full image annotation, where the goal is to accurately segment all object and stuff regions in an image. We propose an interactive, scribble-based annotation framework which operates on the whole image to produce segmentations for all regions. This enables sharing scribble corrections across regions, and allows the annotator to focus on the largest errors made by the machine across the whole image. To realize this, we adapt Mask-RCNN into a fast interactive segmentation framework and introduce an instance-aware loss measured at the pixel-level in the full image canvas, which lets predictions for nearby regions properly compete for space. Finally, we
compare to interactive single object segmentation on the COCO panoptic dataset. We demonstrate that our interactive full image segmentation approach leads to a 5% IoU gain, reaching 90% IoU at a budget of four extreme clicks and four corrective scribbles per region.
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Deep Generative Models for Distribution-Preserving Lossy Compression
Michael Tschannen
Advances in Neural Information Processing Systems (NeurIPS) (2018)
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We propose and study the problem of distribution-preserving lossy compression. Motivated by the recent advances in extreme image compression which allow to maintain artifact-free reconstructions even at very low bitrates, we propose to optimize the rate-distortion tradeoff under the constraint that the reconstructed samples follow the distribution of the training data. Such a compression system recovers both ends of the spectrum: On one hand, at zero bitrate it learns a generative model of the data, and at high enough bitrates it achieves perfect reconstruction. Furthermore, for intermediate bitrates it smoothly interpolates between matching the distribution of the training data and perfectly reconstructing the training samples. We study several methods to approximately solve the proposed optimization problem, including a novel combination of Wasserstein GAN and Wasserstein Autoencoder, and present strong theoretical and empirical results for the proposed compression system.
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