A Study on Frame Prediction Method based on Operation Probability Map

동영상내에서 손상에 의해 소실된 프레임을 복원하거나 연속적인 새로운 프레임을 생성하는 기법인 프레임 예측은 객체들의 동작 예측이 필요한 자율주행, 보안 등의 미래 주요 기술로서 주목받고 있다. 최근 이 기술은 딥러닝 기술과 결합하여 예측 정확도가 많이 향상되고 있으나 많은 학습데이터와 연산량이 수반되기 때문에 실질적인 적용에는 어려움이 존재한다. 기존의 딥러닝 기반 예측 모델은 새로운 프레임 생성 과정에서 예측에 의해 생성된 프레임을 피드백하기 때문에 누적오차가 많이 발생하여 시간이 지남에 따라 예측 정확도가 감소한다. 따라서 본 논문에서는 convolution neural network (CNN)와 long short-term memory (LSTM)으로 구성된 네트워크를 통해 프레임들의 동작 특징들을 추출하고 패턴을 학습하여 동작 확률 지도를 생성하여 움직임이 발생한 영역에 대하여 deconvolution neural network(DNN)를 통해 이후 프레임을 생성하는 새로운 프레임 예측 모델을 제안한다. 제안한 모델은 CNN과 LSTM을 통해 프레임들의 동작 특징들을 추출하고 패턴을 학습하여 동작 확률 지도를 생성한다. 이를 통해 임의의 한 프레임에서 동작이 발생하는 영역를 판별하고 이 영역만 DNN을 통해 새로운 프레임을 획득한다. 이때 학습 난이도가 높은 DNN의 효율적인 학습을 위해 generative adversarial nets(GAN) 기법을 적용한다. 제안된 새로운 모델의 학습과 검증을 위하여 무작위로 일부 프레임이 제거된 로봇 움직임 영상을 기반으로 생성된 영상과 원본 영상을 PSNR로 비교 분석하였다. 그 결과, 제안한 프레임 예측 모델의 PSNR은 35.16으로 비교한 3개의 다른 모델에 비해 최대 14.06이 향상되었다. 또한 생성된 프레임에 따른 PSNR의 감소도 4번째 프레임 이전에는 2, 이후에는 7로 평균 5가 개선되었다.|Frame prediction, which is a technique to reconstruct frames lost due to damage or to generate new consecutive frames in the video, is attracting attention as a main technology which is indispensable for the autonomous vehicle and the artificial intelligence based security system that require motion prediction of objects. Recently, this technology has improved prediction accuracy in combination with deep learning technology, but it is difficulties in practical application because it involves a lot of learning data and computation amount. The existing deep learning based prediction model, since the frame generated by the prediction is feedback in the new frame generation process, is decreased the prediction accuracy over time. Therefore, in this paper, we propose an operation probability map based new frame prediction model using convolution neural network (CNN), long short-term, memory (LSTM) and deconvolution neural network(DNN) to minimize unnecessary computation regions in the frame and prediction error. The proposed model extracts the operating characteristics of the frames through CNN and LSTM and learns the patterns to generate the operation probability map. Through this process, a region in which an operation occurs is determined in one frame, and a new frame is obtained through DNN only in this region. At this time, the generative adversarial nets(GAN) technique is applied for efficient learning of DNN with the high learning complexity. For the learning and verification of the proposed new model, we compared and analyzed the generated frame and the original frame based on robotic motion images with some frames removed randomly using PSNR. As a result, the PSNR of the proposed frame prediction model is 35.16, which is 14.06 higher than the other three models. Also, the decrease of the PSNR according to the generated frame is decreased to 2 before the 4th frame and then to 7 thereafter, and is improved by 5 on the average.Chapter 1 Introduction 01 Chapter 2 Related Works 06 2.1 Convolution Neural Network 06 2.2 Long Short-Term Memory 09 2.3 Generative Adversarial Nets 12 Chapter 3 The Proposed Prediction Model 15 3.1 Structure of the proposed model 17 3.2 Model for feature extraction and operation probability estimation 21 3.3 Model for generating and combining images 24 3.4 Model for learning of generative model 27 Chapter 4 Experiment and Result 29 4.1 Dataset for learning and testing 29 4.2 Analysis of experimental results 30 Chapter 5 Conclusion 37 Reference 38Maste

Novel Motion Anchoring Strategies for Wavelet-based Highly Scalable Video Compression

This thesis investigates new motion anchoring strategies that are targeted at wavelet-based highly scalable video compression (WSVC). We depart from two practices that are deeply ingrained in existing video compression systems. Instead of the commonly used block motion, which has poor scalability attributes, we employ piecewise-smooth motion together with a highly scalable motion boundary description. The combination of this more “physical” motion description together with motion discontinuity information allows us to change the conventional strategy of anchoring motion at target frames to anchoring motion at reference frames, which improves motion inference across time. In the proposed reference-based motion anchoring strategies, motion fields are mapped from reference to target frames, where they serve as prediction references; during this mapping process, disoccluded regions are readily discovered. Observing that motion discontinuities displace with foreground objects, we propose motion-discontinuity driven motion mapping operations that handle traditionally challenging regions around moving objects. The reference-based motion anchoring exposes an intricate connection between temporal frame interpolation (TFI) and video compression. When employed in a compression system, all anchoring strategies explored in this thesis perform TFI once all residual information is quantized to zero at a given temporal level. The interpolation performance is evaluated on both natural and synthetic sequences, where we show favourable comparisons with state-of-the-art TFI schemes. We explore three reference-based motion anchoring strategies. In the first one, the motion anchoring is “flipped” with respect to a hierarchical B-frame structure. We develop an analytical model to determine the weights of the different spatio-temporal subbands, and assess the suitability and benefits of this reference-based WSVC for (highly scalable) video compression. Reduced motion coding cost and improved frame prediction, especially around moving objects, result in improved rate-distortion performance compared to a target-based WSVC. As the thesis evolves, the motion anchoring is progressively simplified to one where all motion is anchored at one base frame; this central motion organization facilitates the incorporation of higher-order motion models, which improve the prediction performance in regions following motion with non-constant velocity