VSNR: a wavelet-based visual signal-to-noise ratio for natural images, Image quality assessment using human visual DOG model fused with random forest, The unreasonable effectiveness of deep features as a perceptual metric, 2022 Deep AI, Inc. | San Francisco Bay Area | All rights reserved. [PKU] Yueyu Hu,Wenhan Yang, Jiaying Liu: Coarse-to-Fine Hyper-Prior Modeling for Learned Image Compression. Image denoising: can plain neural networks compete with BM3D? ICPR 2021. under configs/. Implementation Details. while deep learning-based image compression methods have shown impressive coding performance, most existing methods are still in the mire of two limitations: (1) unpredictable compression. With the development of deep learning techniques, the combination of deep learning with image compression has drawn lots of attention. This publicly available image set is commonly used to evaluate image compression algorithms and IQA models. Note that this list only includes newer publications. S.S. Channappayya, A.C. Bovik, and R.W. Moreover, their convexity properties (Channappayya et al., 2008; Brunet et al., 2012) makes them feasible targets for optimization. Image Compression: ML Techniques and Applications with the combination of a perception loss and an adversarial loss additionally. For example, if we seek to find if . We also used a subset of the Tecnick dataset (Asuni and Giachetti, 2014) containing 100 images of resolution 12001200, and 223 billboard images collected from the Netflix library (Sinno et al., 2020), yielding images having more diverse resolutions and contents. With the exponential growth of the multimedia data, it is inevitable to develop another lossy image compression algorithms with higher performance (a.k.a., using tiny compressed size but presenting high-quality reconstruction). Generally, we initially encode the image performing convolutions on the original image set using techniques outlined in [7] (and similar to those in above models) and then update our encodings using the GAN. These results show that our optimization approach is able to successfully optimize a deep image compression model over different IQA algorithms. Edit social preview. Therefore, this review paper discussed how to apply the rule of deep learning to various neural networks to obtain better compression in the image with high accuracy and minimize loss and. A 2019 Guide to Deep Learning-Based Image Compression Photo by Jose Maria Cuellar on Unsplash Compression involves processing an image to reduce its size so that it occupies less space. Improved Deep Image Compression with Joint Optimization of Cross TCSVT 2021. As may be seen, fp is incorporated into the training of the compression network. AAAI 2020. Arxiv. Recent papers and codes related to deep learning/deep neural network based image compression and video coding framework. Deep image compression performs better than conventional codecs, such as JPEG, on natural images. Networks, Improved Lossy Image Compression with Priming and Spatially Adaptive Bit (b) our proposed architecture with ReLU modifications. In my approach, I changed the training dataset, and modified the model Additionally, as pointed out in (Cheng et al., 2019a), the conventional distortion types in public domain databases are generally quite different from distortions created by a deep neural networks. Deep Image Compression in the Wavelet Transform Domain Based on High Find software and development products, explore tools and technologies, connect with other developers and more. This can be understood by considering the training of the network to be an interpolation problem, whereby the neural networks maps a test image to an accurate quality score. using deep learning. content adaptive fMaps, Lagrangian optimized rate-distortion adaptation, linear piecewise rate estimation, image visual quality enhancement with adversarial loss and perceptual loss included, and so on. Then, install deep-image-compression package. included in the training sets, to avoid overfitting problems. For example, if you EasyChair Preprint no. Arxiv. feature maps (fMaps) at the bottleneck layer for subsequent quantization and entropy coding. [SFU/Google] M. Akbari, J. Liang, J. Han: DSSLIC: Deep Semantic Segmentation-based Layered Image Compression. [paper], [Technical University of Munich] A. Burakhan Koyuncu, Han Gao, Eckehard Steinbach: contextformer: A Transformer with spatio-channel attention for context modeling in learned image compression. ICIP 2018. CVPR 2018. [SJTU] Guo Lu, Chunlei Cai, Xiaoyun Zhang, Li Chen, Wanli Ouyang, Dong Xu, Zhiyong Gao: Content Adaptive and Error Propagation Aware Deep Video Compression. This example shows how to reduce JPEG compression artifacts in an image using a denoising convolutional neural network (DnCNN). . Lastly, the distortion levels that were used for BD-rate calculation were quantified using PSNR, SSIM, MS-SSIM (also represented by MSIM in the table), and VMAF. Chen (2010), Perceptual rate-distortion optimization using structural similarity index as quality metric. ICCV 2019. ompression-decompression task involves compressing data, sending them using low internet traffic usage, and their further decompression. In this paper we tackle the problem of stereo image compression, and leverage the fact that the two images have overlapping fields of view to further compress the representations. Hwang, and N. Johnston (2018), Variational image compression with a scale hyperprior, Efficient nonlinear transforms for lossy image compression, S. Bosse, D. Maniry, K.-R. Muller, T. Wiegand, and W. Samek (2018), Deep neural networks for no-reference and full-reference image quality assessment, J. Bruna, P. Sprechmann, and Y. LeCun (2016), Super-resolution with deep convolutional sufficient statistics, D. Brunet, E.R. If nothing happens, download GitHub Desktop and try again. Luckily, training a proxy network on an existing model does not require human-labeled subjective quality scores such as mean opinion scores (MOS), which is often the greatest obstacle to learning DNN-based IQA models (Ghadiyaram and Bovik, 2016; Kim et al., 2017; Ying et al., 2020). The most well known image compression algorithms are JPEG and its successors JPEG 2000. An end-to-end learning framework based deep image compression scheme is detailed in this work, with innovations among residual unit, Shaham, T. Michaeli: Deformation Aware Image Compression. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. [BUAA] Zhihao Hu, Zhenghao Chen, Dong Xu, Guo Lu, Wanli, Ouyang and Shuhang Gu: Improving Deep Video Compression by Resolution-adaptive Flow Coding. Circuits Syst. Then the de-quantized feature coefficients XQ/(2Q1) is fed into the decoder to finally reconstruct the image signals. The repo has been tested on Nvidia GTX 1070 (8GB memory). Conditional Probability Models for Deep Image Compression | IEEE Hang Chen. CVPR 2021. IMIP 2019. Simpler weight quantization methods exist reducing the size of the model and its energy . [paper], [USTC] Yefei Wang, Dong Liu, Siwei Ma, Feng Wu, Wen Gao: Ensemble Learning-Based Rate-Distortion Optimization for End-to-End Image Compression. [USTC] Z. Chen, T. He, X. Jin, F. Wu: Learning for video compression. Very deep convolutional networks for large-scale image recognition. For example, the denoising task aims to reconstruct a noise-free image from well-maintained, Get health score & security insights directly in your IDE, connect your project's repository to Snyk, Keep your project free of vulnerabilities with Snyk, bin/model_inference_decompress_my_approach, The metrics in the table is averaged on all images from Kodak dataset, The encoding and decoding time are manually recorded. Visit Snyk Advisor to see a Image Compression Based on Deep Learning: A Review - ResearchGate Arxiv. The authors use the additional network to estimate the standard deviation of the quantized coefficients to further improve coding efficiency. repo on a vacant GPU. [Google] D. Minnen, G. Toderici, S. Singh, S. J. Hwang, M. Covell: Image-Dependent Local Entropy Models for Learned Image Compression. The spatial size is reduced by a factor of, The image compression network comprises an analysis transform (ga) at the encoder side, and a synthesis transform (gs, ) at the decoder side. J. Johnson, A. Alahi, and F.-F. Li (2016), Perceptual losses for real-time style transfer and super-resolution, N. Johnston, D. Vincent, D. Minnen, M. Covell, S. Singh, T. Chinen, S.J. This directly reflects the problem we have mentioned (Sec. Gool (2019), Generative adversarial networks for extreme learned image compression, TESTIMAGES: a large-scale archive for testing visual devices and basic image processing algorithms, Proc. Moreover, this paper proposes a more accurate and more concise model based on U-Net, which consists of five pairs of encoder and decoder. Here, we propose a Recently, lossy image compression models have been realized using deep neural network architectures. [paper], [SenseTime Research] Dailan He, Yaoyan Zheng, Baocheng Sun, Yan Wang, Hongwei Qin: Checkerboard Context Model for Efficient Learned Image Compression. compression in TensorFlow repo in [U-Tokyo] Zhisheng Zhong, Hiroaki Akutsu, Kiyoharu Aizawa: Channel-Level Variable Quantization Network for Deep Image Compression. Arxiv. We followed the original work in (Ball et al., 2017), where the rate loss is defined by. Image Compression Using Principal Component Analysis (PCA) 18 downloads a week. In practice, the loss computed from the high-level features extracted from a pre-trained VGG classification network (Simonyan and Zisserman, 2015), , also called VGG loss, has been commonly adopted for diverse computer vision tasks. By applying the proposed alternating training, the proxy network is capable of spontaneously adapting to newly generated adversarial patches. Deep-Learning Based Image Enhancement and Compression However, deep image compression is learning-based and encounters a problem: the compression performance deteriorates significantly for out-of-domain images. This vector can then be decoded to reconstruct the original data (in this case, an image). Deep Image Compression with Iterative Non-Uniform Quantization measurement of loss in neural networks due to their simplicity and analytical The Adam solver (Kingma and Ba, 2015) were used to optimize both the proxy network and the deep compression network, with parameters (1,2)=(0.9,0.999) and a weight decay of 0.01. [paper], [Hosei University] Chi D. K. Pham, Chen Fu, Jinjia Zhou: Deep Learning Based Spatial-Temporal In-Loop Filtering for Versatile Video Coding. After applying PCA on image data, the dimensionality has been reduced by 600 dimensions while keeping about 96% of the variability in the original image data! TIP 2021. Arxiv. Arxiv. The input image formats used were YUV444 for JPEG and JPEG2000, and both YUV420/444 for intra-coded HEVC, respectively. All of the models were trained using NVIDIA 1080-TI GPU cards. IEEE Asilomar Conf. See the full [INRIA] T. Dumas, A. Roumy, C. Guillemot: Image compression with stochastic winner-take-all auto-encoder. CVPR 2021. Using deep learning with MR images of deformed spinal cords as the training data, we were able to segment compressed spinal cords from DCM patients with a high concordance with expert manual segmentation. [Dartmouth] M. H. Baig, V. Koltun, L. Torresani: Learning to Inpaint for Image Compression. The quantization method of Deep Compression uses clustering to compute shared values as new weights. Thus, the networks were trained on 2.1M iterations of back-propagation. Arxiv. applied to train an end-to-end optimized image compression network. VSI: a visual saliency-induced index for perceptual image quality assessment, L. Zhang, L. Zhang, X. Mou, and D. Zhang (2011), FSIM: a feature similarity index for image quality assessment, R. Zhang, P. Isola, A.A. Efros, E. Shechtman, and O. Wang (2018), H. Zhao, O. Gallo, I. Frosio, and J. Kautz (2017), Loss functions for image restoration with neural networks, ProxIQA: A Proxy Approach to Perceptual Optimization of Learned Image hasn't seen any new versions released to PyPI in the Learning a successful CNN model depends highly on the size of the training set. Sort by Weight . To achieve better rate-distortion optimization (RDO), we also introduce an Also, the corresponding metric scores were calculated as the ground-truth for training. ICLR 2017. Here, we review related studies that are closely related to perceptual optimization. [NYU] J. Ball, V. Laparra, E. P. Simoncelli: End-to-end optimized image compression. However, comparing Fig. CVPR 2018. 3. We just skip the Round, The actual bitrates depend on the entropy of the quantized feature maps. PyPI package deep-image-compression, we found that it has been Zendo is DeepAI's computer vision stack: easy-to-use object detection and segmentation. LichengXiao2017/deep-image-compression - GitHub Then, entropy coders such as variable length coding or arithmetic coding can be used to losslessly encode the discrete-valued data into the bitstream during the inference. If nothing happens, download Xcode and try again. When the input image is transformed from the spatial pixel domain to the wavelet transform domain, one low-frequency sub-band (LF sub-band) and three high-frequency sub-bands (HF sub-bands) are generated. Iteration [i] runs the decoder on B [i] to generate a reconstructed image P [i]. [ETH Zurich] Maurice Weber, Cedric Renggli, Helmut Grabner, Ce Zhang: Lossy Image Compression with Recurrent Neural Networks: from Human Perceived Visual Quality to Classification Accuracy. [Google] N. Johnston, D. Vincent, D. Minnen, M. Covell, S. Singh, T. Chinen, S. J. Hwang, J. Shor, G. Toderici: Improved lossy image compression with priming and spatially adaptive bit rates for recurrent networks. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Deep Pressure Sensory Support - This compression vest for autism and sensory processing disorders creates a warm, supportive hug for children or adults who struggle with focus, stress, or anxiety. Zemel (2017), Learning to generate images with perceptual similarity metrics, S. Wang, A. Rehman, Z. Wang, S. Ma, and W. Gao (2012), SSIM-motivated rate-distortion optimization for video coding, Z. Wang, A.C. Bovik, H.R. ECCV 2018. A tool to build, train and analyze deep learning models for image compression. This list is maintained by the Future Video Coding team at the University of Science and Technology of China (USTC-FVC). Trans CSVT. The proxy network is first learned to predict the metric score given a pristine patch and a distorted patch. ICLR 2019. With proper modifications of the framework parameters or the architecture of the proxy network, the approach has the potential to improve on a wide variety of image restoration problems with weak MSE based ways of optimization. t J.Ball, V.Laparra, and E.P. Simoncelli. Huang, N. Ahuja, and M.-H. Yang (2019), Fast and accurate image super-resolution with deep laplacian pyramid networks. ICCV 2019. Next, the trained proxy network is inserted into the loss layer of the deep compression network with the goal of maximizing the proxy score. [Tucodec Inc] XiangJi Wu, Ziwen Zhang, Jie Feng, Lei Zhou, Junmin Wu: End-to-end Optimized Video Compression with MV-Residual Prediction. [UT-Austin] Sheng Cao, Chao-Yuan Wu, Philipp Krhenbhl: Lossless Image Compression through Super-Resolution. easy-to-hard transfer learning when adding quantization error and rate Due to the rapid development of satellite imaging sensors, high-resolution images are being generated for use. quantitative perceptual models. [paper], [Peking University] Yi Ma, Yongqi Zhai, and Ronggang Wang: DeepFGS: Fine-Grained Scalable Coding for Learned Image Compression. Images record the visual scene of our natural world and are often In addition, we use a rate estimation module to approximate the derivable rate loss for back propagation during the training step. Set i=i+1 and go to Step 3 (up to the desired number of iterations). Image segmentation usually serves as the pre-processing before pattern recognition, feature extraction, and compression of the image. Vision Pattern Recog. Evaluation Datasets. Autoencoders are a deep learning model for transforming data from a high-dimensional space to a lower-dimensional space. [Yonsei University] Hanbin Son, Taeoh Kim, Hyeongmin Lee, and Sangyoun Lee: Enhanced Standard Compatible Image Compression Framework based on Auxiliary Codec Networks. Arxiv. Image segmentat. To address this problem, we propose a content-adaptive optimization framework; this framework uses a pre-trained compression model and adapts the model to a target image during compression. Image compression optimized for 3D reconstruction by utilizing deep neural networks. ArXiv. Indeed, significant BD-rate reductions were obtained in many cases. Compress Deep Learning Model with Pruning As you already know, Neural Networks are replicating the process of the brain. China Compresa fra Electric relajante del Cuerpo de tejido profundo [paper], [Northwestern Polytechnical University] Fei Yang, Luis Herranz, Yongmei Cheng, Mikhail G. Mozerov: Slimmable Compressive Autoencoders for Practical Neural Image Compression. averaged bitrate reduction of 28.7% over MSE optimization, given a specified Recent experimental studies suggest that the features extracted from a well-trained image classification network have the capability to capture information useful for other perceptual tasks, where i denotes the output feature map of the i-th layer with Ni elements of a pre-trained network . 3, the network fp, may be as simple as a shallow CNN consisting of three stages of convolution, ReLU nonlinearity, and subsampling. We denote an optimized compression model for a given IQA model M using (7) and (8) by Mp. ICLR 2017. Most apparent distortion: full-reference image quality assessment and the role of strategy, C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A.P. Robust methods trained with domain adaptation or elaborately designed constraint to learn from noisy labels collected from real-world data. It may be observed that the time complexity of the MSE-optimized and VMAFp-optimized models are nearly identical, as they deploy the same network architecture in application. Mach. ICIGP 2019. ICASSP 2018. Explainable deep learning for image/video quality assessment, restoration and compression. We introduce the parameter to control the penalty of rate loss LR which is generated from the rate estimation module as shown in Eq. 9.6K subscribers Content Description In this video, I have explained on how to use autoencoder for image compression using deep cnn model. Are you sure you want to create this branch? [Google] George Toderici, Damien Vincent, Nick Johnston, Sung Jin Hwang, David Minnen, Joel Shor, Michele Covell: Full Resolution Image Compression with Recurrent Neural Networks. [ETHZ] F. Mentzer, E. Agustsson, M. Tschannen, R. Timofte, L. V. Gool: Practical Full Resolution Learned Lossless Image Compression. Furthermore, the rate should be as small as possible. Image-based 3D Object Reconstruction State-of-the-Art and trends in the Then we applied PAQ (a lossless entropy coding method) for the quantized feature cofficients XQ to generate the binary stream. End to End Video Compression Based on Deep-Learning. Here, we present more details on how to train CNNs used in the work. [paper], [Nanjing University] Ming Lu and Zhan Ma: High-Efficiency Lossy Image Coding Through Adaptive Neighborhood Information Aggregation. Last updated on September 16, 2022 by Mr. Yanchen Zuo and Ms. [BUAA] Jiaheng Liu, Guo Lu, Zhihao Hu, Dong Xu: A Unified End-to-End Framework for Efficient Deep Image Compression. In our model, we empirically set =1.54e3 and Mmax=100 when optimizing for VMAF. In the end, we merge the different loss functions to build the final measurement component: We evaluate our performance on the dataset released by CLIC and Kodak PhotoCD data set, and compare with existing codecs including JPEG, JPEG2000, and BPG. Learn more. Papers With Code is a free resource with all data licensed under. ECCV 2020. Deep Image/Video Compression - GitHub Pages We choose the pixel-shuffle layers as up-sampled operations considering its decent performances in super resolution (SR). The performances of all of the codecs were compared to the same baseline the MSE-optimized BLS model. ArXiv. [paper], [Sejong University] Khawar Islam, Dang Lien Minh, Sujin Lee, Hyeonjoon Moon: Image Compression with Recurrent Neural Network and Generalized Divisive Normalization. An interesting observation can be made that, unlike using other IQA models used as targets of the proposed optimization, VMAFpoptimization delivers coding gain with respect to all of the BD-rate measurements, except the PSNR BD-rate. The compression algorithm tries to find the residual information between the video frames. The proxy IQA network fp takes a reference patch x and a distorted patch ^x as input, where both have WH pixels. [Qualcomm AI Research] Amirhossein Habibian, Ties van Rozendaal, Jakub M. Tomczak, Taco S. Cohen: Video Compression With Rate-Distortion Autoencoders. Intel Developer Zone [Waseda University] Song Zebang, Kamata Sei-ichiro: Densely connected AutoEncoders for image compression. Tans CSVT. Adapters are inserted into the decoder of the model. Figure 5 shows a visual comparison under extreme compression (around 0.05 bpp). proposed by Balle et al in "Variational Image Compression with a Scale In practice, we use the open source data sets released by the Computer Vision Lab of ETH Zurich in CLIC competition. [ETH Zurich] Ren Yang, Fabian Mentzer, Luc Van Gool, Radu Timofte: Learning for Video Compression with Recurrent Auto-Encoder and Recurrent Probability Model. Learning convolutional networks for content-weighted image Arxiv. The decoder has a symmetrical architecture to reconstruct the signal from the compressed fMaps. It is critical for a learned image compression model to have comparable execution time to other codecs. Arxiv. [paper], [Tohoku University] Shoma Iwai, Tomo Miyazaki, Yoshihiro Sugaya, and Shinichiro Omachi: Fidelity-Controllable Extreme Image Compression with Generative Adversarial Networks. Arxiv. [2003.02012] Asymmetric Gained Deep Image Compression With Continuous [paper], [Microsoft Research Asia] Jiahao Li, Bin Li, Yan Lu: Deep Contextual Video Compression. image compression method, derived from H.265, available in iPhone and Mac) and It's suggested that you specify [paper], [University of Texas] Li-Heng Chen, Christos G. Bampis, Zhi Li, Lukas Krasula, and Alan C. Bovik: Estimating the Resize Parameter in End-to-end past 12 months, and could be considered as a discontinued project, or that which Arxiv. 2c, respectively. Beyond these early efforts, other recent approaches have adopted more complex network architectures such as recurrent neural networks (RNNs), In fact, the idea of optimizing conventional codecs such as JPEG or H.264/AVC against perceptual models like SSIM, VIF, or VMAF have been deeply studied (Channappayya et al., 2008; Huang et al., 2010; Wang et al., 2012; Lu et al., 2020) and implemented in widespread practice (Li et al., 2018). It should be noted that none of these test images were In addition to the BLS model, we also deployed the proposed VMAFpoptimization framework on a more sophisticated deep compression model (Ball et al., 2018) (BMSHJ) to test its generality. for deep-image-compression, including popularity, security, maintenance Image compression is one the applications. [CAS] Xiaojun Jia, Xingxing Wei, Xiaochun Cao, Hassan Foroosh: ComDefend: An Efficient Image Compression Model to Defend Adversarial Examples. However, when the reconstructed patches were feed into the proxy network along with their objective quality scores, the proxy network is updated straightaway to predict proxy quality much more accurately. Moorthy, J.D. Unfortunately, severe complication can arise when applying this straightforward methodology. [FUDAN] Yi Xu, Longwen Gao, Kai Tian, Shuigeng Zhou, Huyang Sun: Non-Local ConvLSTM for Video Compression Artifact Reduction. compression. IIB-CPE: Inter and Intra Block Processing-Based Compressible Perceptual Source: Variable Rate Deep Image Compression With a Conditional Autoencoder Benchmarks Add a Result These leaderboards are used to track progress in Image Compression Show all 11 benchmarks Libraries To calculate BD-rate, we encoded the images at eight different bitrates, ranging from 0.05 bpp (bit per pixel) to 2 bpp. Given a mini-batch pair x and ^x collected from the most recent update of the compression network, the quality scores M(x,^x) are calculated. [University of Bristol] Fan Zhang, Mariana Afonso, David Bull: Enhanced Video Compression Based on Effective Bit Depth Adaptation. Deep Image Compression is an end-to-end tool for extreme image compression Image compression optimized for 3D reconstruction by utilizing deep Some image compression techniques also identify the most significant components of an image and discard the rest, resulting in data compression as well. Sajjadi, B. Scholkopf, and M. Hirsch (2017), EnhanceNet: single image super-resolution through automated texture synthesis, Very deep convolutional networks for large-scale image recognition, Z. Sinno, A.K. NIPS 2017. Most of these have employed deep auto-encoders. Deep Image Compression is an end-to-end tool for extreme image compression using deep learning. ArXiv. for feature extraction. especially at a very low bit rate. the GPU you are going to use before running the scripts. Thus the package was deemed as Under this scheme, Ld is the residual between the source patch and the reconstructed patch mapped by d(. To adapt images with different content, we dynamically control the bit rates by using different network models. Use Git or checkout with SVN using the web URL. We select four typical images with different types from the dataset as test samples, as shown in Fig. Trans CSVT. [ETH Zurich] Ren Yang, Fabian Mentzer, Luc Van Gool, Radu Timofte: Learning for Video Compression with Hierarchical Quality and Recurrent Enhancement. Table1 tabulates the benchmark study on the aformentioned three datasets. [UT-Austin] Li-Heng Chen, Christos G. Bampis, Zhi Li, Andrey Norkin, Alan C. Bovik: Perceptually Optimizing Deep Image Compression. neural networks (CNNs), which outperforms the existing BPG, WebP, JPEG2000 and Impact of image compression on deep learning-based mammogram - Nature There was a problem preparing your codespace, please try again. [paper], [USTC] Haichuan Ma, Dong Liu, Cunhui Dong, Li Li, Feng Wu: End-to-End Image Compression with Probabilistic Decoding. CVPR 2019. For each input image, our framework optimizes the latent representation extracted by the encoder and the adapter parameters in terms of rate-distortion. Note that Ball [2] also applied similar idea to do joint rate and distortion optimization. However, continuous rate adaptation remains an open question. Thin arrows indicate the flow of data in the network, while bold arrows represent the information being delivered to update the complementary network. DeepSIC: Deep Semantic Image Compression | SpringerLink [WaveOne] O. Rippel, L. Bourdev: Real-time adaptive image compression. Perceptually Optimizing Deep Image Compression | DeepAI [FIU] Zihao Liu, Qi Liu, Tao Liu, Nuo Xu, Xue Lin, Yanzhi Wang, Wujie Wen: Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial Examples. ICLR 2020. Cock, Z. Li, and A.C. Bovik (2020), Quality measurement of images on mobile streaming interfaces deployed at scale, J. Snell, K. Ridgeway, R. Liao, B.D. Deep Learning Model Compression for Image Analysis: Methods - Medium
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