Title: DRASIC: DISTRIBUTED RECURRENT AUTOENCODER FOR SCALABLE IMAGE COMPRESSION

Abstract: We propose a new architecture for distributed image compression from a group of distributed data sources. The work is motivated by practical needs of datadriven codec design, low power consumption, robustness, and data privacy. The proposed architecture, which we refer to as Distributed Recurrent Autoencoder for Scalable Image Compression (DRASIC), is able to train distributed encoders and one joint decoder on correlated data sources. Its compression capability is much better than the method of training codecs separately. Meanwhile, the performance of our distributed system with 10 distributed sources is only within 2 dB peak signal-to-noise ratio (PSNR) of the performance of a single codec trained with all data sources. We experiment distributed sources with different correlations and show how our methodology well matches the Slepian-Wolf Theorem in Distributed Source Coding (DSC). Our method is also shown to be robust to the lack of presence of encoded data from a number of distributed sources. Moreover, it is scalable in the sense that codes can be decoded simultaneously at more than one compression quality level. To the best of our knowledge, this is the first datadriven DSC framework for general distributed code design with deep learning.

1. INTRODUCTION:

It has been shown by a variety of previous works that deep neural networks (DNN) can achieve comparable results as classical image compression techniques (Toderici et al., 2015; Ballé et al., 2016; Gregor et al., 2016; Toderici et al., 2017; Theis et al., 2017; Johnston et al., 2017; Liu et al., 2018; Li et al., 2018; Mentzer et al., 2018) . Most of these methods are based on autoencoder networks and quantization of bottleneck representations. These models usually rely on entropy codec to further compress codes. Moreover, to achieve different compression rates it is unavoidable to train multiple models with different regularization parameters separately, which is often computationally intensive.

In this work, we are motivated to develop an architecture that has the following advantages. First, unlike classical distributed source coding (DSC) which requires customized code design for different scenarios (Xiong et al., 2004) , a data-driven distributed compression framework can handle nontrivial distribution of image sources with arbitrary correlations. Second, the computation complexity of encoders (e.g. mobile devices) can be transferred to the decoder (e.g. a remote server). Such a system of low complexity encoders can be used in a variety of application domains, such as multi-view video coding (Girod et al., 2005) , sensor networks (Xiong et al., 2004) , and under-water image processing where communication bandwidth and computational power are quite restricted (Stojanovic & Preisig, 2009; Schettini & Corchs, 2010) . Third, the distributed framework can be more robust against heterogeneous noises or malfunctions of encoders, and such robustness can be crucial in, e.g., unreliable sensor networks (Girod et al., 2005; Ishwar et al., 2005; Xiao et al., 2006) . Last but not least, the architecture is naturally scalable in the sense that codes can be decoded at more than one compression quality level, and it allows efficient coding of correlated sources which are not physically co-located. This is especially attractive in video streaming applications (Guillemot et al., 2007; Gehrig, 2008) .

It is tempting to think that splitting raw data into different encoders compromises the compression quality. It is thus natural to ask this question: Can distributed encoders perform as well as a single encoder trained with all data sources together? A positive answer from a theoretical perspective was given in the context of information theory, where DSC is an important problem regarding the compression of multiple correlated data sources. The Slepian-Wolf Theorem shows that lossless coding of two or more correlated data sources with separate encoders and a joint decoder can compress data as efficiently as the optimal coding using a joint encoder and decoder (Slepian & Wolf, 1973; Cover, 1975) . The extension to lossy compression with Gaussian data sources was proposed as Wyner-Ziv Theorem (Wyner & Ziv, 1976) . Although these theorems were published in 1970s, it was after about 30 years that practical applications such as Distributed Source Coding Using Syndromes (DISCUS) emerged (Pradhan & Ramchandran, 2003) . One of the main advantages of DSC is that the computation complexity of the encoder is transferred to the decoder. A system architecture with low complexity encoders can be a significant advantage in applications such as multi-view video coding and sensor networks (Girod et al., 2005; Xiong et al., 2004) .

Motivated by the theoretical development of DSC, in this work we propose a DNN architecture that consists of distributed encoders and a joint decoder (illustrated in Fig. 1 and 2 ). We show that distributed encoders can perform as well as a single encoder trained with all data sources together. Our proposed DSC framework is data-driven by nature, and it can be applied to distributed data even with unknown correlation structure.

The paper is outlined below. We review previous related works in Section 2. We describe our proposed architecture for general image compression and its basic modules in Subsections 3.1-3.4. Then we elaborate the Deep Distributed Source Coding framework in Subsection 3.5. Experimental results are shown in Section 4, followed by conclusions in Section 5.

2. RELATED WORK:

Though there has been a variety of research on lossy data compression in the past few decades, little attention has been paid to a systematic approach for general and practical distributed code design, especially in the presence of an arbitrary number of nontrivial data sources with arbitrary correlations (Xiong et al., 2004) . A main motivation of this work is to attempt to replace the practical hand-crafted code design with data-driven approaches. To our best knowledge, what we propose is the first data-driven DSC architecture. Unlike hand-crafted quantizers, our neural network-based quantizers show that the correlations among different data sources can be exploited by the model parameters. Inspired by DSC, We empirically show that it is possible to approach the theoretical limit with our methodology.

2.1. IMAGE COMPRESSION WITH DEEP LEARNING:

There exist a variety of classical codecs for lossy image compression. Although the JPEG standard (Wallace, 1992) was developed thirty years ago, it is still the most widely used image compression method. Several extensions to JPEG including JPEG2000 (Skodras et al., 2001 ), WebP (Google, 2010) and BPG (Bellard, 2014) have been developed. Most of these classical codecs rely on a quantization matrix applied to the coefficients of discrete cosine transform or wavelet transform.

Common deep neural network architecture for image compression are auto-encoders including nonrecurrent autoencoders (Ballé et al., 2016; Theis et al., 2017; Li et al., 2018; Mentzer et al., 2018) and recurrent autoencoders (Toderici et al., 2015; 2017; Johnston et al., 2017) . Non-recurrent autoencoders use entropy codec to encode quantized bottleneck representations, and recurrent models introduce incremental binarized codes at each compression quality. The generated codes of nonrecurrent models is not scalable and their performance heavily relies on the conditional generative model like PixelCNN (Van den Oord et al., 2016) which arithmetic coding can take advantage of (Li et al., 2018; Mentzer et al., 2018) . Recurrent autoencoders, on the other hand, can reconstruct images at lower compression qualities with the subset of high quality codes. Other notable variations include adversarial training (Rippel & Bourdev, 2017) , multi-scale image compression (Nakanishi et al., 2018) , and generalized divisive normalization (GDN) layers (Ballé et al., 2016) . Another challenge is to well define the derivative of quantizations of bottleneck representations. Ballé et al. (2016) replaced non-differentiable quantization step with a continuous relaxation by adding uniform noises. Toderici et al. (2015) , on the other hand, used a stochastic form of binarization.

2.2. DISTRIBUTED SOURCE CODING:

Our methodology is inspired by the information-theoretic results on DSC which have been established since 1970s. The Slepian & Wolf (1973) Theorem shows that two correlated data sources encoded separately and decoded jointly can perform as well as joint encoding and decoding, and outperform separate encoding and separate decoding. The striking result indicates that as long as the codes are jointly decoded, there can be no loss in coding efficiency even the codes are separately encoded. Cover (1975) generalizes the achievability of Slepian-Wolf coding to arbitrary number of correlated sources. Wyner & Ziv (1976) Coding gives a rate-distortion curve as an extension to lossy cases.

A classical illustration of Slepian-Wolf achievable region is shown in Fig. 3 . We can achieve the performance of joint encoding and decoding of two data sources X and Y where the bit rate R is equal to the joint entropy H(X, Y ) with separate encoding and joint decoding. Specifically, the achievable region proved by the Slepian-Wolf Theorem is given by R

X ≥ H(X|Y ), R Y ≥ H(Y |X), and R X + R Y ≥ H(X, Y )

as shown in the shaded area of Fig. 3 . Here R • and H(•) denote the bit rates and (conditional) entropies in classical Shannon theory. In practice, although some works are proposed to approach the mid-point C (Schonberg et al., 2004) , the most widely used scheme is source coding with side information (syndrome bits) at the decoder (Pradhan & Ramchandran, 2003) . This code design takes advantage of the corner points A and B which correspond to H(X, Y ) = H(Y ) + H(X|Y ) and H(X, Y ) = H(X) + H(Y |X) respectively. Some researchers have also shown the applicability of DSC on still images (Dikici et al., 2005) . In practical applications, low complexity video encoding benefits from the DSC framework which can transfer the complexity of encoder to decoder (Puri & Ramchandran, 2002; Aaron et al., 2002) . Scalable Video Coding can also be incorporated with DSC (Xu & Xiong, 2006) . These proposed methods indicate the feasibility of DSC in our problem setting.

3. METHODS:

In this section, we first describe the recurrent autoencoder for scalable image compression used in our work. We will elaborate the basic modules including Pixel (Un)Shuffle, and Binarizer used in our model. We will then describe how this Deep Learning architecture is used in Distributed Source Coding framework.

3.1. NETWORK ARCHITECTURE:

Our compression network consists of an encoder, a binarizer, and a decoder. The activation function following each Convolutional Neural Network (CNN) module is tanh. For the first iteration of our model, the input images are initially encoded and transformed into (-1, 1) by tanh activation function. Binary codes are quantized from bottleneck representations. The decoder then reconstructs images based on the received binary codes. Finally, we compute the residual difference between the original input images and the reconstructed output images. At the next iteration, the residual difference is feedback as the new input for our model. This procedure is repeated multiple iterations to gain more codes for better reconstruction performance. Therefore, the reconstructed images at each iteration are the sum of output reconstructions from previous and current iterations. The dependencies among iterations are modeled by recurrent models like ConvLSTM. We iterate 16 times to generate scalable codes. Compared to non-scalable codes which require new set of codes at each compression quality, scalable codes are able to reconstruct images at lower compression quality by using the subset of codes. This is especially attractive in video streaming applications (Guillemot et al., 2007; Gehrig, 2008) .

EQUATION

Deep recurrent autoencoder gradually increases compression quality by creating a correlated residual sequence from the difference between the input and output of our model. The advantage of recurrent model is that we can use a subset of generated codes to reconstruct images at lower compression qualities. Classical autoencoders, on the contrary, not only have to train multiple networks with different penalty coefficients for rate-distortion loss but also have to generate different codes for different compression quality. Suppose T iterations are used, we can formulate the recurrent autoencoder in the following way.

z t = f (x t ; θ), zt = Binarize(z t ), (3) xt = g(z t ; φ), x t+1 = x t -xt , x1 = 0, (4) Minimize 1 T T t=1 L(x 1 , t i=1

xi ).

(5)

3.2. PIXEL (UN)SHUFFLE:

We resize feature maps with Pixel UnShuffle modules. Pixel Shuffle module is originally proposed by Shi et al. (2016) to tackle image and video super-resolution problem. Compared to transposed convolutional layers, Pixel Shuffle module is computationally efficient, because it is non-parametric and only requires tensor reshaping and dimension permutation (Shi et al., 2016) . We note that although this method is used for upscaling, it is actually invertible and we propose to use its inversion for downscaling. Thus, the encoder and decoder can be constructed symmetrically. Our experimental results show that symmetric recurrent autoencoder architecture actually produces better results with less number of parameters, compared to the asymmetric architecture using transposed convolutional layers as proposed in Toderici et al. (2017) . We describe the module with the following pseudocodes.

Algorithm 1 Pixel UnShuffle

Require: X ∼ (N, C, H, W ), r H , r W Ensure: r H , r W are integers, divides H, W Reshape X ∼ (N, C, H/r H , r H , W/r W , r W ) Permute X ∼ (N, C, r H , r W , H/r H , W/r W ) Reshape X ∼ (N, C × r H × r W , H/r H , W/r W )

3.3. BINARIZER:

The derivative of quantization function is only defined at the rounded integer itself. Therefore, we have to replace its derivative in the backward pass of backpropagation with a form of smooth approximation (Rumelhart et al., 1988) . Thanks to a thorough discussion of different alternative approaches by (Theis et al., 2017) , we choose to use the identity function to replace its derivatives that cannot be well defined as shown in 6. During training, we use a stochastic form of binarization proposed by (Toderici et al., 2017) . For bottleneck representations z ∈ (-1, 1), the details of binarizer z = Binarize(z) are described as follows.

Train

EQUATION

3.4 DEEP DISTRIBUTED SOURCE CODING FRAMEWORK Fig. 1 and 2 illustrate our Distributed Recurrent Autoencoder for Scalable Image Compression (DRASIC). Similar to classical DSC framework, each data source is encoded separately and decoded jointly. In our network, each distributed encoder in Fig. 1 has the exact same structure in Fig. 2 . Traditionally, researchers have to design different kind of codes for specific data sources (Schonberg et al., 2004) . We propose to use data-driven approach to handle complex scenarios where the distribution of data sources is unknown and their correlations can be arbitrary. Our proposal may also shed new light on sophisticated application scenarios such as videos where data sources and correlations are time dependent.

In sources with the same model parameters φ. In classical settings, the joint decoder has to process all compressed codes from each source jointly. In our data-driven setting, the joint training process optimizes the model such that the single decoder can decode from correlated sources. In this case, decoding codes from a particular data source does not depend on synchronization of codes from other sources, since the model has been optimized to adapt the correlations among all sources.

EQUATION

EQUATION

Minimize

EQUATION

Our result shows that the resulting distributed model can perform as well as encoding all data by one single encoder. However, if we encode and decode each data source separately, the performance becomes significantly worse, i.e. with xm t = g(z m t ; φ m ).

4. EXPERIMENTS:

To show our model is capable of compressing natural images, we train our model on CIFAR10 dataset (Stojanovic & Preisig, 2009) and evaluate the rate-distortion curve on Kodak dataset (Franzén, 2002) . To show our model is capable of compressing grayscale images and demonstrate the feasibility of training encoders in a distributed manner, we train and evaluate our models with MNIST dataset LeCun et al. (1998) with peak-to-noise-ratio (PSNR) against bit per pixel (BPP). We observe that many non-recurrent autoencoders outperform recurrent models on rate-distortion curves (Li et al., 2018; Mentzer et al., 2018) . We emphasize the distinction between the recurrent and non-recurrent autoencoders which do not have the scalability of reconstructing low quality images by using the subset of codes for high quality reconstruction. Our experiments aim to empirically demonstrate the feasibility of scalable distributed source coding in a data-driven setting. We use Adam optimizer (Kingma & Ba, 2014) with minibatch size of 100 for all experiments. We use learning rate 0.001 for a total of 200 epochs and decay every 50 epochs by a factor of 0.5. Our model uses a depth size D = 3 such that a 32×32 image will be compressed into 8×4×4 binarized codes at each iteration. We iterate our models 16 times to achieve the compression rates from 0.125 to 2 BPP. We evaluate our model . We empirically found that L 1 loss performs much better than L 2 and binary cross entropy loss. Fig. 4a shows that our symmetric recurrent autoencoder performs comparable to classical codecs and neural network-based codecs on compressing natural images, and performs significantly better on compressing handwritten grayscale images.

To demonstrate the feasibility of compressing distributed data sources, we split our data into correlated subsets to emulate the case where encoders only have access to distributed correlated data sources. We conduct our experiments with (2, 4, 8, 10) number of distributed sources. For the MNIST dataset, the correlated data sources are from images separated by random subsets and class labels. Data source split by class labels only contains the images of the same digit. First, we compare our result, labeled as Distributed, to the case where all data are trained with one encoder and one decoder jointly, labeled as Joint. The Joint curve is approximated as the theoretical upper bound of performance. Second, we compare our result to the case where each data source is trained with a separate pair of encoder and decoder, labeled as Separate. Although we split our training data, we evaluate each of them with all test data. In Fig. 5a and 5b, we illustrate the Pearson's correlation matrix among MNIST images split by random subsets and labels. It shows that the pixels of MNIST images are moderately correlated. Inspired by DSC, it is therefore possible to take advantage of their dependencies by training distributed encoders and a joint decoder. Our experimental studies in the following sections consist of three aspects. We first experiment (2, 4, 8, 10) number of distributed data sources with different correlations. We then show the robustness of our distributed framework in the absence of a number of distributed sources. Finally, we show the performance of low complexity encoders which is trained with less number of iterations.

4.1. ARBITRARY CORRELATED SOURCES:

To address the advantage of our DNN-based DSC framework, we experiment distributed sources with different correlations. The distributed encoders are labeled as 1, 2, . . . , m. For example, when m = 2, we only use first two subsets of images of digit 0 and 1. We show the result of data sources distributed by random subsets in Fig. 6 , 8 and by class labels in Fig. 7, 9 . The curves of distributed encoders show that the performance of training distributed encoders and joint decoder can be very close to the theoretical limit. As the number of encoders grows, the performance decreases a little, but still dominantly outperforms training codecs for each data source separately. From various experiments we found that the gap will become smaller when more data are available. The gap also diverges as more bits are generated. This is because the residual differences used as input at each iteration are less correlated among data sources than the original images. Results of images split by random subsets also outperform images split by class labels, it may relate to the constant correlation as shown in Fig. 5a . This experiment clearly shows that near-oracle performance can be approached without specifically estimating the correlations among different sources, a desirable feature not enjoyed by classical DSC code design. The results show that our Deep DSC framework can benefit from dependencies among an arbitrary number of data sources.

4.2. ROBUST DISTRIBUTED ENCODING:

Our data-driven DSC framework, unlike classical DSC code design, does not require synchronization of data sources. In classical DSC code design, if syndrome bits H(X|Y ) are used and the data source Y is accidentally blocked, we will not be able to decode the data source X. In our data-driven framework, even only one of the distributed encoders is functional, it can still benefit from its dependencies with other sources because their dependencies are already trained by the model parameters.

All our experiments show that distributed encoders not only dominate separately trained codecs but also have narrower confidence bands. As the number of encoders increases, the confidence bands of separately trained codecs become wider because each separate codec can only access very limited amount of data and thus suffer from overfitting. On the other hand, the confidence band of distributed encoders remain small because the model parameters can capture the dependencies among correlated data sources. As the number of iterations increases, the confidence bands also become wider. This is because the residual differences at later iterations become less correlated.

4.3. LOW COMPLEXITY ENCODING:

We also demonstrate the performance of low complexity encoders which are trained with less number of iterations. In this experiment, the first half of encoders is trained with 16 iterations, labeled as Full, and the second half of encoders is only trained with 8 iterations, labeled as Half. For example, for m = 8, encoders 1 to 4 are trained with 16 iterations while encoders 5 to 8 are trained with 8 iterations. In Fig. 6 and Fig. 7 , we show the dashed lines for T = 8 and solid lines for T = 16 respectively. The theoretical limits (in black lines) are trained with all available data. The first half of encoders and the second half of encoders only access half of the whole dataset respectively.

Half complexity encoders perform as well as full complexity encoders in the first 8iterations, because their dependencies of the first eight iterations are trained properly with the model parameters. After the eighth iteration, both full and half complexity encoders can still approach their theoretical limits. However, without specifically training dependencies after the eighth iterations, the performance of half complexity encoders is also worse than encoders trained in a distributed manner.

5. CONCLUSION:

We introduced a data-driven Distributed Source Coding framework based on Distributed Recurrent Autoencoder for Scalable Image Compression (DRASIC). Compared to classical code design, our method has the following advantages. First, instead of explicitly estimating the correlations among data sources in advance, we use data-driven approach to learn the dependencies with the neural network parameters. Given enough training data, our method can handle an arbitrary number of sources with arbitrary correlations. Second, we showed the robustness of our framework. Unlike classical code design which may require careful data source synchronization, each distributed encoder of our model, once trained and deployed, can be used independently of others because the dependencies are already learned by the model parameters. Third, as one of the most important applications of Distributed Source Coding, low complexity encoders were shown to be feasible based on our experimental results. Data sources trained with less data and fewer number of iterations can still approach the theoretical limit obtained by pulling all the data. Last but not least, our recurrent model can reconstruct images efficiently even at low compression quality.

We point out two interesting directions of future work. First, the compression quality of the proposed architecture may be improved by introducing spatially adaptive weights over different iterations, e.g. by using context models for adaptive arithmetic coding. Second, the network architecture may be further extended to handle time-dependent data sources.

:



Figures and Tables:

Figure 1: Illustration of Deep Distributed Source Coding.

Figure 2: Illustration of Recurrent Autoencoder for Scalable Image Compression.

Figure 3: The Slepian-Wolf achievable region for two sources X and Y .

Figure 4: Our symmetric recurrent autoencoder performs comparable to classical codecs and neural network-based codecs.

(a) Split by random subsets. (b) Split by class labels.

Figure 5: Pearson's correlation matrix among MNIST dataset.

Figure 6: Rate-distortion curves for MNIST distributed by random subsets with T = 16 for all sources.

Figure 7: Rate-distortion curves for MNIST distributed by class labels with T = 16 for all sources.

Figure 8: Rate-distortion curves for MNIST distributed by random subsets with T = 16 for the first half of sources and T = 8 for the second half of sources.

Figure 9: Rate-distortion curves for MNIST distributed by class labels with T = 16 for the first half of sources and T = 8 for the second half of sources.

Peer review (iclr2020):
The paper proposed a distributed recurrent auto-encoder for image compression that uses a ConvLSTM to learn binary codes that are constructed progressively from residuals of previously encoded information. Experiments show that the proposed model achieved better PSNR than other compression auto-encoders and algorithms. The paper claims distributed representation has advantage for image compression. The idea in the paper is a good one, except for the part about *distributed*. 2 main concerns here: 1. The paper uses the word *distributed* to mean multi-source or clustered data inputs. This is different from what the deep learning community normally means, and the paper has the risk of misleading readers without clarification or using a different terminology. Why not multi-source / multi-modal? 2. By analyzing figure 4 and figure 5 together, it seems that the PSNR results are similar between multi-source / clustered data and normal auto-encoder. The paper mentioned that the multi-source model may have advantage over information corruption and balance between encoder/decoder model sizes, but no experiments are shown to demonstrate that. This rendered the motivation of the whole *distributed* part unclear. Since the *distributed* seems to occupy a significant portion of the paper, I vote for rejection based on the current manuscript.