RTSR: Enhancing Real-time H.264 Video Streaming using Deep Learning based Video Super Resolution Spring 2017 CS570 Project Presentation June 8, 2017
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1 RTSR: Enhancing Real-time H.264 Video Streaming using Deep Learning based Video Super Resolution Spring 2017 CS570 Project Presentation June 8, 2017 Team 16 Soomin Kim Leslie Tiong Youngki Kwon Insu Jang Wonyoung Lee 1
2 Index 1. Problem and Related Works 2. Approach 3. Super Resolution Machine Learning Models and Data 4. Experiments and Results 5. Conclusion 2
3 Problem In real-time video streaming, for high quality video transmission, video compression technique is needed compression technique causes video quality loss 3
4 Super-Resolution (SR) Super-Resolution technique makes image more sharper Sharp Image Super-Resolution 4
5 Single Image Super-Resolution Accurate Image Super-Resolution Using Very Deep Convolutional Networks Kim et al. (CVPR16) Fast super-resolution technique but it s target is not video 5
6 Video Super-Resolution Video super-resolution with convolutional neural networks Kappeler et al. (TCI16) The processing time is about 10 seconds per frame. -> not suitable real-time video processing 6
7 Goal Propose real-time video streaming framework to combine H.264 codec and super-resolution technique for preserving video quality Encoded Video Decoded Video SR model 7
8 Approach Exploit data structure used in video compression: Group of Picture (GOP) How H.264 codec reduces the size of video? A video containing 3 frames. Pixel information for home is duplicated
9 Approach Exploit data structure used in video compression: Group of Picture (GOP) How H.264 codec reduces the size of video? H.264 does not store home in 2 nd and 3 rd frame
10 Approach Exploit data structure used in video compression: Group of Picture (GOP) How H.264 codec reduces the size of video? H.264 does not store home in 2 nd and 3 rd frame I-frame P-frame or B-frame
![Approach Exploit data structure used in video compression: Group of Picture (GOP) GOP: Group of frames (I-, P-, or B-) There should be 1 I-frame in GOP 4~8 frames in a GOP is ideal for LTE [1] 30~60](/docs-images/76/73114902/images/11-0.jpg "frames in a GOP in practical [2, 3] [1] Zulpratita, Ulil S. \"GOP length effect analysis on H. 264/AVC video streaming transmission quality over LTE network.\" (2013).")
11 Approach Exploit data structure used in video compression: Group of Picture (GOP) GOP: Group of frames (I-, P-, or B-) There should be 1 I-frame in GOP 4~8 frames in a GOP is ideal for LTE [1] 30~60 frames in a GOP in practical [2, 3] [1] Zulpratita, Ulil S. "GOP length effect analysis on H. 264/AVC video streaming transmission quality over LTE network." (2013). [2] Twitch TV, Broadcast Requirements. [Online] [3] Youtube Help, Live encoder settings, bitrates, and resolutions. [Online]
12 Approach Performing super resolution on video.. Compressed form I P P B P P Decoded form Frame Frame Frame Frame Frame Frame Super Resolution SR SR SR SR SR SR Performing super resolution for every frame SR cannot be done in a frame interval High computation overhead
13 Approach Performing super resolution on video.. Compressed form I P P B P P Decoded form Frame Frame Frame Frame Frame Frame Super Resolution SR SR SR SR SR SR Performing super resolution for every frame SR cannot be done in a frame interval High computation overhead Frame interval 0.03s VDSR 0.1s SRCNN 2s A+ 0.9s
14 Approach Performing super resolution on video.. Compressed form I P P B P P Decoded form Frame Frame Frame Frame Frame Frame Super Resolution SR SR SR SR SR SR Performing super resolution for every frame SR cannot be done in a frame interval High computation overhead Frame interval 0.03s VDSR 0.1s SRCNN 2s A+ 0.9s
15 Approach Our approach: perform super resolution on I-frame only Compressed form I P P B P P Super Resolution SR-I Decoded form Frame Frame Frame Frame Frame Frame Performing super resolution for I-frame only SR can be done in GOP interval (0.5s ~ 2s) All frames are affected by SR I-frame (as they reference it)
16 Learning based SR - Algorithm (VDSR) Learn residual only for fast convergence Input : interpolated low resolution image Interpolated LR HR Residual image learning LR Learning HR
17 Learning based SR - Algorithm (VDSR) Learn residual only for fast convergence Input : interpolated low resolution image Residual image learning LR HR LR Learning Training dataset x #, y # #&' Define residual image r = y x Goal is to minimize ' r f x - - ( x: interpolated LR y: HR r: residual image f: network prediction f(x)+x: final image
18 Learning based SR - Model (VDSR) Interpolated LR Conv.1 ReLu.1 Conv.D-1 ReLu.D-1 learned residual HR Skip-connection way CNN to SR 20 convolutional layers Skip connection to learn residual only No dimension reduction such as pooling Accurate Image Super-Resolution Using Very Deep Convolutional Networks by Kim et al. CVPR 2016
19 Learning based SR - Training Data (VDSR) Training data 291 images with data augmentation(rotation or flip) 91 from Yang et al.[1] from Berkeley segmentation dataset[2] [1] Image superresolution via sparse representation by Yang et al. TIP, 2010 [2] A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. by Martin et al., ICCV, 2001
![Implementation Target movie: all videos with 24 fps, GOP length=12 Movie Player: Simple FFmpeg based player [link] H.](/docs-images/76/73114902/images/20-0.jpg "264 Decoder: FFmpeg [link] SR Module: Pytorch VDSR [link] with pre-trained checkpoint Communication: Message queue btw SR module / FFmpeg Request every 0.5s Request every 0.")
20 Implementation Target movie: all videos with 24 fps, GOP length=12 Movie Player: Simple FFmpeg based player [link] H.264 Decoder: FFmpeg [link] SR Module: Pytorch VDSR [link] with pre-trained checkpoint Communication: Message queue btw SR module / FFmpeg Request every 0.5s Request every 0.04s Pytorch Super Resolution Module Request SR for I-frame IPC SR applied I-frame FFmpeg Decoder Request decode Decoded frame FFmpeg based player
21 Performance Measurement 1. Peak Signal-to-Noise Ratio (PSNR) Most commonly used to measure the quality of reconstruction of lossy compression codecs Commonly used metric for super resolution 2. Time Delay We argued that performing super resolution for all frames cannot be done in real-time. Measure how the delay is in (1) SR for all frames, (2) ours
22 Interpret Results PSNR Comparison Higher is better RTSR Original PSNR RTSR (Ours) Original 22
23 Interpret Results Time Delay Comparison Playtime compared to original video Closer to 100% is better 100% 138% RTSR (Ours) RTSR SR for every frames Time Delay SR for every frames 23
24 Discussion 1. Contribution Applied SR with Deep Learning in real-time streaming to reconstruct highresolution videos. Our proposed model only applied SR to i-frames, which is outperformed without time-delayed than applied SR to all frames. 2. Limitation The high quality of reconstructed video is depending on original video sources. 24
25 Demo Test Video 1 RTSR (ours) Original 25
26 Demo Test Video 2 RTSR (ours) Original 26
27 Thank you. Q&A 27
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