George Toderici
George Toderici is a research scientist / TLM of the Neural Compression team in Google Research. He and his team are exploring new methods for compression of multimedia content using techniques inspired from the neural network domain. Previously he has worked on video classification tasks based on classical methods as well as more modern neural network-based methods. Dr. Toderici has been involved in organizing the first and second Workshop and Challenge on Learned Image Compression (CLIC 2018-2021 at CVPR), the first and second YouTube-8M workshop at CVPR 2017, ECCV 2018, ICCV 2019, the THUMOS 2014 workshop at ECCV, and is one of the co-authors of the Sports-1M and Atomic Video Actions (AVA) datasets. Previously he has served as a Deep Learning area co-chair for ACM Intl. Conf. on Multimedia (MM) in 2014, In addition, he has served in the program committees of CVPR, ECCV, ICCV, ICLR and NIPS for numerous years. His research interests include deep learning, action recognition and video classification.
Authored Publications
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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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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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LVAC: Learned Volumetric Attribute Compression for Point Clouds using Coordinate Based Networks
Phil Chou
Sung Jin Hwang
Journal of Frontiers in Signal Processing (2022)
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We propose the first learned compression framework, LVAC, for volumetric functions represented by implicit networks -- a.k.a. coordinate-based networks (CBNs). In order to evaluate LVAC and compare it with prior (traditional) methods, we specifically focus on compressing point cloud attributes since there are no compression baselines for other signals' CBN-based representations. LVAC serves as the first baseline for them. More concretely, we consider the attributes of a point cloud as samples of a vector-valued volumetric function at discrete positions. To compress the attributes given the positions, we compress the parameters of the volumetric function. We represent the volumetric function by shifts of a CBN, or implicit neural network. Inputs to the network include both spatial coordinates and a latent vector per shift. To compress the latent vectors, we perform an end-to-end training of the overall pipeline where the latent vectors are rate-distortion optimized by back-propagation through a rate-distortion Lagrangian loss in an auto-decoder configuration. The result outperforms the current standard, RAHT, by 2--4 dB.
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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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End-to-end Learning of Compressible Features
Abhinav Shrivastava
2020 IEEE Int. Conf. on Image Processing (ICIP)
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Pre-trained convolutional neural networks (CNNs) are very powerful as an off the shelf feature generator and have been shown to perform very well on a variety of tasks. Unfortunately, the generated features are high dimensional and expensive to store: potentially hundreds of thousands of floats per example when processing videos. Traditional entropy based lossless compression methods are of little help as they do not yield desired level of compression while general purpose lossy alternatives (e.g. dimensionality reduction techniques) are sub-optimal as they end up losing important information. We propose a learned method that jointly optimizes for compressibility along with the original objective for learning the features. The plug-in nature of our method makes it straight-forward to integrate with any target objective and trade-off against compressibility. We present results on multiple benchmarks and demonstrate that features learned by our method maintain their informativeness while being order of magnitude more compressible.
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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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Improved Lossy Image Compression with Priming and Spatially Adaptive Bit Rates in Recurrent Convolutional Neural Networks
Damien Vincent
Michele Covell
Sung Jin Hwang
Joel Shor
The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)
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We propose a method for lossy image compression based on recurrent, convolutional neural networks that outperforms BPG (4:2:0), WebP, JPEG2000, and JPEG as measured by MS-SSIM. We introduce three improvements over previous research that lead to this state-of-the-art result using a single model. First, we show that training with a pixel-wise loss weighted by SSIM increases reconstruction quality according to several metrics. Second, we modify the recurrent architecture to improve spatial diffusion, which allows the network to more effectively capture and propagate image information through the network’s hidden state. Finally, in addition to lossless entropy coding, we use a spatially adaptive bit allocation algorithm to more efficiently use the limited number of bits to encode visually complex image regions. We evaluate our method on the Kodak and Tecnick image sets and compare against standard codecs as well recently published methods based on deep neural networks.
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Joint Autoregressive and Hierarchical Priors for Learned Image Compression
Advances in Neural Information Processing Systems 31 (2018)
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Recent models for learned image compression are based on autoencoders that learn approximately invertible mappings from pixels to a quantized latent representation. The transforms are combined with an entropy model, which is a prior on the latent representation that can be used with standard arithmetic coding algorithms to generate a compressed bitstream. Recently, hierarchical entropy models were introduced as a way to exploit more structure in the latents than previous fully factorized priors, improving compression performance while maintaining end-to-end optimization. Inspired by the success of autoregressive priors in probabilistic generative models, we examine autoregressive, hierarchical, and combined priors as alternatives, weighing their costs and benefits in the context of image compression. While it is well known that autoregressive models can incur a significant computational penalty, we find that in terms of compression performance, autoregressive and hierarchical priors are complementary and can be combined to exploit the probabilistic structure in the latents better than all previous learned models. The combined model yields state-of-the-art rate–distortion performance and generates smaller files than existing methods: 15.8% rate reductions over the baseline hierarchical model and 59.8%, 35%, and 8.4% savings over JPEG, JPEG2000, and BPG, respectively. To the best of our knowledge, our model is the first learning-based method to outperform the top standard image codec (BPG) on both the PSNR and MS-SSIM distortion metrics.
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Image-dependent local entropy models for image compression with deep networks
Sung Jin Hwang
Michele Covell
International Conference on Image Processing (2018)
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The leading approach for image compression with artificial neural networks (ANNs) is to learn a nonlinear transform and a fixed entropy model that is directly optimized for rate-distortion performance. We show that this approach can be significantly improved by incorporating spatially local, image-dependent entropy models. The key insight is that existing ANN-based methods learn an entropy model that is shared between the encoder and decoder, but they do not transmit any side information that would allow the model to adapt to the structure of a specific image. We present a method for augmenting ANN-based image coders with image-dependent side information that leads to a 17.8% rate reduction over a state-of-the-art ANN-based baseline model on a standard evaluation set, and 70-98% reductions on images with low visual complexity that are poorly captured by a fixed, global entropy model.
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