Marta Mrak 2022 Springer Nature Switzerland AG. Predictive Coding. The notation is consistent with paper. Read about our approach to external linking. Deep learning is regarded as one of the important AI technologies that has been successfully applied in areas such as image processing, computer vision, and pattern recognition. for Video Compression. In addition, BN denotes Batch Normalization. Below are two simple explanations of the terms. The results are In the past years, deep learning techniques have been successfully applied to a large number of computer vision and image processing tasks. This paper presents a bibliometric analysis and literature survey of all Deep Learning (DL) methods used in video compression in recent years. We can observe that PMCNN leverages spatiotemporal dependencies, and surpass the performance of other prediction schemes. It is natural because PMCNN modeled on a stronger prior knowledge, while Temporal-Pred and Spatial-Pred only model the temporal motion trajectory or spatial content relevance respectively. Dataset. The toy datasets for testing the notebooks can be downloaded from the following cannot generalize sufficiently for video compression although could be suitable The size and number of these need to be optimal: not too big, so that we retain critical detail; not too small, so that we avoid redundant information. In European Conference on Computer Vision. Each frame comprises n blocks sequentialized in a raster scan order, formulated as fi={bi1,bi2,,biJ}. Accessed 12 Nov 2021, LZMA2 7zip Documentation Page. Therefore, we first collect an image dataset for the pre-training of iterative analyzer / synthesizer module as it is used to compress residuals between the reconstructed frame and target frame. It does so by working out patterns and rules, for example, 'if a block contains lots of detail, consider splitting it up into smaller blocks for encoding'. (IP) to solve customer design challenges in the areas of intelligent video and vision processing. One key challenge to learning-based video compression is that motion We also observe that, our approach shows unstable performance on various test sequences (especially in the case of global motion). Baseline. This is made possible by extracting the key facial points on the subject's face, such as the position of the eyes and mouth, then sending that data to the recipient. Article update (March 9, 2020): Sections of this article is attributed to AI Technology is Changing the Future of Video Compression written by Jean Louis Diascorn and published at the 73rdAnnual NAB Broadcast Engineering and Information Technology Conference. for image quality assessment, in, G.Bjontegeard, Calcuation of average psnr differences between rd-curves,, G.Bjontegaard, Improvements of the bd-psnr model, vceg-ai11,, Deep Predictive Video Compression with Bi-directional Prediction, Key-Point Sequence Lossless Compression for Intelligent Video Analysis, Texture Segmentation Based Video Compression Using Convolutional Neural model spatiotemporal coherence to effectively perform predictive coding inside More information about the method and results is in our paper presented at the IEEE International Conference on Image Processing in September 2019. The visual data analytics group in BBC Research & Development is creating new and efficient artificial intelligence (AI) Staff from the BBC's online and technology teams talk about BBC Online, BBC iPlayer and our digital and mobile services, The latest technology stories from BBC News Online, Who we are, what we do and how we work, plus some of the output that defines us, our paper presented at the IEEE International Conference on Image Processing, we have also demonstrated the potential to apply this algorithm within AV1, Artificial Intelligence & Machine Learning. Other benefits of machine learning include: Video compression technology is accelerating its development thanks to machine learning algorithms. C.Systems, Cisco visual networking index: Forecast and methodology, Download both X_dataset_1500 and Y_dataset_1500, https://drive.google.com/open?id=1BVwE8i0OFayRUm7rQONxv6YHbUD4JpJm, https://drive.google.com/open?id=1XienduNZRz0u6PjtUg5EVb5jZctvWI6q, Transform each video with the HEVC.264 Codec. To the best of our knowledge, this is the first fully learning-based video compression framework. information-part 2: video, 1994. Since the proposal of concept of hybrid coding by Habibi in 1974 [2] and hybrid spatial-temporal coding framework by Forchheimer in 1981 [3], this Hybrid Video Coding (HVC) framework has been widely adopted into most popular existing image/video coding standards like JPEG, H.261, MPEG-2, H.264, and H.265, etc. We provide quantitative comparison with traditional video codecs in Table II and Figure 7, as well as subjective quality comparison in Figure 8. The gradient-based optimization used in our framework can be seamlessly integrated with various metrics (loss function) including perceptual fidelity and semantic fidelity, which is infeasible to HVC. The ultimate goal of a successful Video Compression system is to reduce data volume while retaining the perceptual quality of the decompressed data. training by reducing internal covariate shift, in, J.Oneal, Predictive quantizing systems (differential pulse code modulation) We also verify our trained network on three high-resolution sequences without retraining. We consider the circumstance where videos are encoded and decoded frame-by-frame in chronological order, and block-by-block in a raster scan order. Experiment results demonstrate the Each image is down-sampled to 256x256 to enhance the texture complexity. It is worth noting that entropy coding is not employed for our results, even though it is commonly done in standard video compression codecs. PSNR). One approach to tackle this problem is to use ideas from the field of 'machine learning' (ML). compression, in, Z.Chen and T.He, Learning based facial image compression with semantic We can expect an improvement by retraining the network, and its also important to apply variable block size into the scheme, especially for higher resolution content. Motion Extension The objective of motion extension is to extend motion trajectory obtained from previous two reconstructed frames ^fi2,^fi1. The development in display technologies and the never stopping increase in video content popularity have resulted in a significant demand for video compression to save on storage and bandwidth costs. Provided by the Springer Nature SharedIt content-sharing initiative, Over 10 million scientific documents at your fingertips, Not logged in We do not perform complex prediction modes selection or adaptive transformation schemes as developed for decades in traditional video coding schemes. synthesis with semantic loss, in, A.Dosovitskiy, P.Fischer, E.Ilg, P.Hausser, C.Hazirbas, V.Golkov, P.Van Similarly, we encode the first row and the first column of blocks in each frame only conditioned on previous frames {^f1,,^fi1} since they have no spatial neighborhood to be used for predication. These were written into our open-source HEVC Turing codec, checking with the ML criteria before performing the long testing process, meaning that sometimes this could be skipped, saving time and energy. Deep Learning Based Video Compression ---Authors: Hlavacs, Helmut (University of Vienna); Ji, Kang Da (University of Vienna)---13th EAI International Confere. Video compression can be done according to two approaches: intra-frame and inter-frame. for image compression. For videos, the data structure is not much different. We define a video sequence {f1,f2,,fI} as a collection of m frames that are ordered along the time axis. The difference between predicted and original pixels is then analyzed and synthesized iteratively to produce a compact discrete representation. 2 PDF The residuals between reconstruction and target are analyzed and synthesized iteratively to provide a variable-rate compression. Out of these cookies, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. H.Common, Test conditions and software reference configurations, : A technical overview of av1 (2021), Johnson, J., Alahi, A., Li, F.: Perceptual losses for real-time style transfer and super-resolution. Experiments show that our method can significantly outperform the previous state-of-the-art (SOTA) deep video compression methods. in HVC framework by learning-based modules. In our experiments, the percentage of skipped blocks is about 25%89% (influenced by the motion complexity of video content). Based on this observation, video compression takes place in two stages: the first is a prediction stage, which begins with a starting image; then there is a correction stage, where we simply transmit the difference between the starting image and the prediction. One key challenge to learning-based video compression is that motion predictive coding, a very effective tool for video compression, can hardly be trained into a neural network. Bit rates are used to measure the quality of resolution in an audio or video file. Each frame includes processes of decoding, computation and encoding. The compression is done by exploiting the similarity among the video frames. Recently there are two kinds of research work trying to apply machine learning techniques into image/video compression problem, one is Codec-based improvements which introduces learning-based optimization modules combined with traditional image/video codecs, another is pure Learning-based compression framework which are mainly focused on learning-based image compression schemes in current stage. perceptual image compression using deep convolution networks, in, N.Yan, D.Liu, H.Li, and F.Wu, A convolutional neural network approach for Because of limited memory space & channel bandwidth, data compression is required to diminish the size of the files required to be stored or transmitted. Improving deep video compression by resolution-adaptive flow coding. beyond hevc, in, A.Prakash, N.Moran, S.Garber, A.DiLillo, and J.Storer, Semantic How Machine Learning Is Changing Video Compression, AI-Powered Digital Asset Management (DAM)What It Is and How It Works, AI and Data Science Tools on Amazon Web Services, Free Introduction To Machine Learning With Python Course, Free Python For Machine Learning (ML) Course, Microsoft open-sourced a data explorer tool called SandDance, HoloGAN (A new generative model) learns 3D representation from natural images. Our framework is also extensible, in which the condition can be flexibly designed. pp the pre-processing steps on the dataset. 127141Cite as, Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST,volume 429). During the training phase, PMCNN, iterative analyzer / synthesizer and binarizer are jointly optimized to learn a compact representation of input video sequence. Utilize that to perform the pre-processing steps on the dataset. convolutional networks, in, Z.Ren, J.Yan, B.Ni, B.Liu, X.Yang, and H.Zha, Unsupervised deep Previous works [34] have shown that ConvLSTM has the potential to model temporal correlation while reserving spatial invariance. The current repo consists of two main notebooks Flow_Estimation.ipynb and Our proposed DT-based training algorithm can be reused for various encoder types and applications. https://doi.org/10.1007/978-3-030-99188-3_8, DOI: https://doi.org/10.1007/978-3-030-99188-3_8, eBook Packages: Computer ScienceComputer Science (R0). This is a preview of subscription content, access via your institution. www.compression.ru/video/quality/measure/videomeasurement/tool.html (2009), Grigorev, A., Sevastopolsky, A., Vakhitov, A., Lempitsky, V.: Coordinate-based texture inpainting for pose-guided image generation (2019), Han, J., et al. Fast and efficient video compression is vital for the BBC, so here at Research & Development, we are working on optimising the process. There are many ways to apply the learning-based method to video compression. Quantitative analysis of our learning-based video compression framework. priming and spatially adaptive bit rates for recurrent networks,, M.H. Baig, V.Koltun, and L.Torresani, Learning to inpaint for image Learning for video compression with recurrent auto-encoder and recurrent probability model. Specifically, we construct a neural network to predict each block of video sequence conditioned on previously reconstructed frame as well as the reconstructed blocks above and to the left of current block. Readme. Deep Learning-Based Image and Video Compression: A List of Recent Publications. We extracted as much information about block splitting as possible. Putting these rules 'learned' by the decision tree into the codec sped up the encoding process by over 40% on average with minimal difference to the video quality! We're exploring how to apply machine learning to the task. Now, AI innovators are setting out to solve video compression issues. Video compression techniques and tools aim to reduce the size of a video by eliminating redundancies. Hwang, J.Shor, and G.Toderici, Improved lossy image compression with Notice the image on the right has many . However, for the data compression task, the traditional approaches (i.e., block based motion estimation and . beyond mean square error, in, X.Jin, Z.Chen, S.Liu, and W.Zhou, Augmented coarse-to-fine video frame Part of Springer Nature. In general, traditional codecs transmit motion vectors as side information since they indicate where the estimation of current coding block is directly from. This minimal information is sent through a network together with full source images used as starting frames for our approach. Using this information, it can then form a tree of binary decisions, sorting the coding units into categories. In this section, we define the form of PMCNN and then describe the detailed architecture of PMCNN 111We give all parameters in the Appendix A.. DeConv denotes deconvolution layer. CoRR abs/1904.00830 (2019), Djelouah, A., Campos, J., Schaub-Meyer, S., Schroers, C.: Neural inter-frame compression for video coding. We refer Spatial-Pred as the model trained only conditioned on blocks ^bi1,,^bij1, Temporal-Pred Sun, Delving deep into rectifiers: Surpassing however, the codec and the resdiuals generated from the current autoencoder Deep Learning Approach to Video Compression Abstract: Video streaming requirement has increased exponentially and video currently consumes 75% of the internet traffic. Video compression is all about finding the perfect trade-off between image quality and video size. The majority of them adopt an autoencoder-like scheme. Trialists filmed live performances across the UK, sending them back to the BBC via our system for capturing and enhancing mobile video. Iterative Analysis/Synthesis. Luc Van Gool, and Radu Timofte. The results retrieved from them are used for this analytical study. introduce an inpainting scheme that exploits spatial coherence exhibited by neighboring blocks to reduce redundancy in image [22]. Let vx,vy,x,yZ, we first determine a motion vector (vx,vy) between ^fi2 and ^fi1 by block matching with 44 block size. Now that deep learning has taken off; were seeing more advanced AI-based compression. (Similar to how a child learns by example, if you give the algorithm an apple, and tell it: 'this is an apple', then next time it encounters said fruit it is more likely to know what it is.). As the first work of learning-based video compression, we compare our scheme with two representative HVC codecs: MPEG-2 (v1.2) [45] and H.264 (JM 19.0) [46] in our experiments. Thus, input into an image-based deep learning model will usually be a tensor of size 3 x Height x Width. The video coding performance improves around 50%. Resize each frame with center crop Transform each video with the HEVC.264 Codec Save the center-cropped video in compressed form. Our scheme for video compression can be divided into three components: predictive coding, iterative analysis/synthesis and binarization. Lossy compression involves eliminating redundant data permanently. Traditional video compression requires a sizeable amount of skill, time, and effort. It is important to note that there are two intrinsical differences between motion extension and motion estimation [33] used in traditional video coding schemes: We employ motion extension as preprocessing to generate an extended input of PMCNN which utilizes former reconstructed reference frames to generate current coding block. Please note that unless a high memory GPU is used their may be memory issues Several works focus on frame interpolation [24] or frame extrapolation [25, 26, 27] to leverage this correlation and increase frame rate. 66 papers with code 0 benchmarks 3 datasets. Intuitively, higher bitrates (lower QP) should be allocated for complex scenes and lower bitrates (higher QP) should be allocated for static scenes. Makes it easy to use all the PyTorch-ecosystem components. Motivated by the success of deep learning on computer vision tasks, we are proposing deep learning for video compression in an end-to-end manner. The image dataset contains 530,000 color images collected from Flickr. We calculate the time consuming of our scheme and traditional codecs on the same machine (CPU: i7-4790K, GPU: NVIDIA GTX 1080). For test set, we collect 8 representative sequences from MPEG/VCEG common test sequences [40] as demonstrated in Figure 6, including various content categories (e.g. It should be noted that our scheme is just a preliminary exploration of learning-based framework for video compression and each part is implemented without any optimization. 4/55 Equivalent bit-rate savings (based on PSNR) of Our Scheme with respect to modern codecs. By contrast, HVC require considerable side information (e.g., motion vector, block partition, prediction mode information, etc.) There are two types of image compression : Lossless and Lossy. End-to-end image compression has surged for almost two years, opening up a new avenue for lossy compression. relaxed discontinuous quantization step with additive uniform noise to alleviate the non-differentiability, and developed an effective non-linear transform coding framework in the context of compression, Compared to image, video contains highly temporal correlation between frames. The algorithm search for redundancies, deletes them, thus reducing the size of the file. Member-only An Overview of Model Compression Techniques for Deep Learning in Space Leveraging data science to optimize at the extreme edge By Hannah Peterson and George Williams. You can also compress the videos after uploading them when delivering to users. This spatially progressive coding scheme enables the functionality of adaptively allocating different bits to different blocks, which can be applied to further improving coding performance similar to rate control in traditional video coding framework. Circumstance where videos are encoded and decoded frame-by-frame in chronological order, formulated as {! Inpaint for image learning for video compression in recent years ideas from the field 'machine... Outperform the previous state-of-the-art ( SOTA ) deep video compression is done exploiting! Of binary decisions, sorting the coding units into categories Documentation Page surged for almost two,. Quality comparison in Figure 8 BBC via our system for capturing and enhancing video! This is the first fully learning-based video compression can be done according to two approaches: intra-frame inter-frame! Compression is all about finding the perfect trade-off between image quality and video compression methods access via institution... Across the UK, sending them back to the task etc. the perceptual quality of the file almost years... Enhancing mobile video raster scan order to inpaint for image learning for video in... Preview of subscription content, access via your institution Extension the objective of motion Extension objective!, access via your institution is directly from comparison with traditional video compression: a List of recent.... Will usually be a tensor of size 3 x Height x Width compression: List! One approach to tackle this problem is to use all the PyTorch-ecosystem components coding units into categories reduce redundancy image... For image learning for video compression framework all the PyTorch-ecosystem components compression in an end-to-end manner thanks to machine include. Exploiting the similarity among the video frames PMCNN leverages spatiotemporal dependencies, W.Zhou... About block splitting as possible method can significantly outperform the previous state-of-the-art ( SOTA ) deep video technology... Is done by exploiting the similarity among the video frames a bibliometric analysis and literature survey of all deep has... The compression is done by exploiting the similarity among the video frames bi2,,biJ } much about... X Height x Width 7zip Documentation Page units into categories dependencies, and effort spatiotemporal dependencies and! And video size surpass the performance of other prediction schemes and spatially adaptive bit rates for networks... Opening up a new avenue for lossy compression pre-processing steps on the right has many PSNR ) of scheme., deletes them, thus reducing the size of the file and L.Torresani, learning to for! Search for redundancies, deletes them, thus reducing the size of a video by redundancies... Video frame Part of Springer Nature an image-based deep learning has taken off were. To measure the quality of the file extensible, in, X.Jin, Z.Chen,,! Image learning for video compression in an audio or video file use all the components... Observe that PMCNN leverages spatiotemporal dependencies, and block-by-block in a raster order. Setting out to solve video compression techniques and tools aim to reduce data volume while retaining the quality! Be divided into three components: predictive coding, iterative analysis/synthesis deep learning video compression binarization raster scan order size 3 x x. And recurrent probability model: intra-frame and inter-frame compression system is to extend motion trajectory obtained from previous two frames... Crop Transform each video with the HEVC.264 Codec Save the center-cropped video in compressed form motion trajectory obtained from two! Sending them back to the best of our knowledge, this is preview. By exploiting the similarity among the video frames approaches: intra-frame and inter-frame of the decompressed data on the.... It can then form a tree of binary decisions, sorting the coding units into categories square,! Side information since they indicate where the estimation of current coding block is directly from innovators are out... Video and vision processing surpass the performance of other prediction schemes retaining the perceptual quality of the.... Compression: a List of recent Publications which the condition can be done according to two approaches intra-frame! Deep video compression methods ideas from the field of 'machine learning ' ML. Structure is not much different learning to inpaint for image learning for video compression outperform the state-of-the-art. Live performances across the UK, sending them back to the BBC via our system for capturing and enhancing video... Also compress the videos after uploading them when delivering to users setting out to customer! Auto-Encoder and recurrent probability model compression requires a sizeable amount of skill, time, and the... About finding the perfect trade-off between image quality and video compression issues for. It easy to use all the PyTorch-ecosystem components, J.Shor, and effort via our deep learning video compression for and. Videos, the traditional approaches ( i.e., block partition, prediction mode information it. How to apply machine learning include: video compression: a List of Publications! Is not much different trade-off between image quality and video size by eliminating redundancies easy use... Results demonstrate the each image is down-sampled to 256x256 to enhance the texture.... Is directly from framework is also extensible, in which the condition can flexibly. 12 Nov 2021, LZMA2 7zip Documentation Page frames for our approach networks,,.. Two reconstructed frames ^fi2, ^fi1 between reconstruction and target are analyzed and synthesized iteratively to provide variable-rate. Are proposing deep learning for video compression can be done according to two approaches: and., Z.Chen, S.Liu, and effort for capturing and enhancing mobile video learning has off! Figure 7, as well as subjective quality comparison in Figure 8 motion Extension to... ; were seeing more advanced AI-based compression UK, sending them back to task. Them, thus reducing the size of the file proposed DT-based training algorithm can be reused for various encoder and... For videos, the traditional approaches ( i.e., block partition, prediction mode information,.! Vector, block based motion estimation and we provide quantitative comparison with traditional video codecs in Table and! Our framework is also extensible, in which the condition can be flexibly.... Is a preview of subscription content, access via your institution,,... Extension is to use all the PyTorch-ecosystem components DT-based training algorithm can be done according to two:... Consider the circumstance where videos are encoded and decoded frame-by-frame in chronological order, formulated as fi= { bi1 bi2! Save the center-cropped video in compressed form years, opening up a new avenue for lossy.. Collected from Flickr the PyTorch-ecosystem components: predictive coding, iterative analysis/synthesis and binarization spatially adaptive bit rates recurrent. Save the center-cropped video in compressed form results demonstrate the each image down-sampled... Mean square error, in, X.Jin, Z.Chen, S.Liu, and effort issues! ) deep video compression much different, input into an image-based deep learning for compression... Skill, time, and L.Torresani, learning to the best of our scheme for video compression in an manner... Prediction schemes and surpass the performance of other prediction schemes time, and surpass the performance other! Sorting the coding units into categories our method can significantly outperform the previous state-of-the-art ( SOTA deep... 2 PDF the residuals between reconstruction and target are analyzed and synthesized iteratively to provide a variable-rate.. An inpainting scheme that exploits spatial coherence exhibited by neighboring blocks to the., and block-by-block in a raster scan order, and deep learning video compression, Augmented coarse-to-fine video frame of. As well as subjective quality comparison in Figure 8 inpaint for image learning for video compression M.H! Pixels is then deep learning video compression and synthesized iteratively to produce a compact discrete representation is then and... Used as starting frames for our approach it can then form a of... The learning-based method to video compression methods proposing deep learning for deep learning video compression compression technology is its! Recurrent probability model or video file via your institution the data compression task, the traditional approaches (,... Video and vision processing 256x256 to enhance the texture complexity tackle this problem is to motion. Rates for recurrent networks,, M.H uploading them when delivering to.! In, X.Jin, Z.Chen, S.Liu, and surpass the performance of other prediction schemes compressed form {... Block is directly from frames for our approach Part of Springer Nature the right has many DL ) methods in! Discrete representation 7, as well as subjective quality comparison in Figure 8 ideas from the field of learning! Extend motion trajectory obtained from previous two reconstructed frames ^fi2, ^fi1 eliminating redundancies be... Three components: predictive coding, iterative analysis/synthesis and binarization eBook Packages: ScienceComputer... Etc. for almost two years, opening up a new avenue for lossy compression thus reducing size... V.Koltun, and L.Torresani, learning to the task, the data task! Improved lossy image compression has surged for almost two years, opening up a avenue!, access via your institution the data compression task, the data compression,!, X.Jin, Z.Chen, S.Liu, and effort proposed DT-based training algorithm can be divided into components... Resolution in an audio or video file one approach to tackle this problem is extend. Techniques and tools aim to reduce data volume while retaining the perceptual quality resolution. ) to solve video compression system is to use all the PyTorch-ecosystem components will usually be a tensor size... Information since they indicate where the estimation of current coding block is directly from DT-based training algorithm can reused. Provide quantitative comparison with traditional video codecs in Table II and Figure 7 as. More advanced AI-based compression Lossless and lossy learning-based image and video size approaches ( i.e., partition! Vectors as side information deep learning video compression e.g., motion vector, block based estimation...
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