video frame prediction


video prediction is defined as: given a sequence of video frames as context, predict the subsequent frames –generation of continuing video given a sequence of previous frames. Different from video generation that is mostly unconditioned, video prediction is conditioned on a previously learned representation from a sequence of input frames.

Video frame prediction by deep multi-branch mask network
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Future frame prediction in video is one of the most important problem in computer vision, and useful for a range of practical applications, such as intention prediction or video anomaly detection. However, this task is challenging because of the complex and dynamic evolution of scene. The difficulty of video frame prediction is to model the inherent spatio-temporal correlation between frames and pose an adaptive and flexible framework for large motion change or appearance variation. In this paper, we construct a deep multi-branch mask network (DMMNet) which adaptively fuses the advantages of optical flow warping and RGB pixel synthesizing methods, i.e., the common two kinds of approaches in this task. In the procedure of DMMNet, we add mask layer in each branch to adaptively adjust the magnitude range of estimated optical flow and the weight of predicted frames by optical flow warping and RGB pixel synthesizing, respectively. In other words, we provide a more flexible masking network for motion and appearance fusion on video frame prediction. Exhaustive experiments on Caltech pedestrian and UCF101 datasets show that the proposed model can obtain favorable video frame prediction performance compared with the state-of-the-art methods. In addition, we also put our model into the video anomaly detection problem, and the superiority is verified by the experiments on UCSD dataset.

Stochastic video prediction with conditional density estimation
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As a result, most video prediction studies focus on video data where frameto- frame transitions are strongly deterministic . Recent developments in deep learning approaches for This model, shown in Figure 1(a), reflects our belief that, in a stochastic video prediction task with Markovian dynamics, the next frame is a function of the previous frame and injected noise.

Video prediction via selective sampling
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Our work involves two key insights: (1) Video prediction can be approached as a stochastic process: we sample a collection of proposals conforming to possible frame distribution at following time stamp, and one can select the final prediction from it. (2) De-coupling combined loss functions into dedicatedly designed sub-networks encourages them to work in a

A Log-likelihood Regularized KL Divergence for Video Prediction With a 3D Convolutional Variational Recurrent Network.
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In this paper, we introduce a new variational model that extends the recurrent network in two ways for the task of video frame prediction . First, we introduce 3D convolutions inside all modules including the recurrent model for future frame prediction inputting and outputting a sequence of video frames at each timestep. This enables us to better exploit spatiotemporal

Adaptive frame prediction for foveation scalable video coding
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Embedded rate scalable video coding allows for the extraction of coded visual information at continuously varying bit rates from a single compressed bitstream. This is a very attractive feature for many multimedia communication applications. Motion estimation (ME)/motion compensation (MC) techniques are widely employed in various video coding systems to

Margin Learning Embedded Prediction for Video Anomaly Detection with A Few Anomalies.
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Therefore, we propose the use of such a scheme to favor the prediction of normal data while distorting the prediction of abnormal data. Specifically, given a video with normal events of length T + we propose to encode each frame of the first T frames with an encoder, then we will sequentially feed these features into a ConvLSTM, which has previously demonstrated its

Video frame interpolation and extrapolation
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The loss function we adopt is l2 error between the true video frame and predicted video frame . Also we choose adam update rule to achieve faster convergence. The learning curve is Rather than simply calculating l2 loss function of the final prediction layer and true frame as in our vanilla model, here we feed the predicted frame and true frame back into the autoencoderprediction (LSP), and demonstrate its potential in video coding. Motivated by the duality between edge contour in images and motion trajectory in video we propose to derive the best prediction of the current frame from It is demonstrated that LSP is particularly effective for modeling video material with slow motion and can be extended to handle fast motion by temporal

Deep action conditional neural network for frame prediction in Atari games
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For example the next observed frame for a robot depends on its input controls. We study this problem of actioned conditioned video prediction through video frame prediction in the classic Atari 2600 video games. In this setting future frames are dependent on past frames as well as actions performed by the players. We use OpenAI Gym along with a Deep Q Network [

Content-Based Video Quality Prediction for MPEG4 Video Streaming over Wireless Networks.
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In addition, video content has an impact on video quality under same network conditions. The main aim of this paper is the prediction of video quality combining the application and This section describes the regression-based video quality prediction model based on the application level parameters of Send Bitrate and Frame Rate and network level parameter ofIn this work, we explore the idea of cross-view video prediction to learn a good representation for action recognition in a multi-view environment. There has been a lot work in view- We have also seen some preliminary success in the research on video prediction and proposed methods are mainly focused on future frame prediction [ 30], future clip prediction [42],

Physics-as-inverse-graphics: Joint unsupervised learning of objects and physics from video
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We apply our model to two challenging tasks: long-term video prediction and visual model-predictive control. First, we evaluate future frame prediction and physical parameter estimation accuracy on 4 datasets with different non-linear interactions (2 objects bouncing off the walls, 2 objects connected by a spring, and 3 objects with gravitational attraction) and differentin the literature as the best solution for finegranularity scalability in wavelet-based video compression, we propose a coding scheme based on the hybrid coding structure with motion ME/MC procedures, the decoder can skip any of the remaining coded frames and achieve any desired framerate for the decoded video reproduction without drifting from the encoder.

End-to-end prediction model of video quality and decodable frame rate for MPEG broadcasting services
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This paper proposes, describes and evaluates a novel theoretical framework for end-to-end video quality assessment of MPEG-based video services in hand-held and mobile wireless broadcast systems. The proposed framework consists two discrete models: A model for predicting the video quality of an encoded signal at a preencoding state by specifying the bit

White paper: an overview of H. 264 advanced video coding
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for video compression, the process of converting digital video into a format that takes up less capacity when it is stored or transmitted. Video compression (or video coding) is an essential technology for applications such as digital television, DVD- Video It defines a format (syntax) for compressed video and a method for decoding this syntax to produce a displayable video

Temporal coherency based criteria for predicting video frames using deep multi-stage generative adversarial networks
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Video frame prediction has recently been a popular problem in computer vision as it caters to a wide range of applications including self-driving cars, surveillance, robotics and in-painting. Performance analysis with experiments of our proposed prediction model for video frame (s) have been done on video clips from Sports-1M , UCF-101 [21] and KITTI datasets.

Three dimensional sub-band coding of video
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The comptational complexity of this coding scheme compares favorably to DCT with interframe prediction and inter/intra frame DPCM. The scheme has an architectural structure suitable We compare the scheme, as described in section 2 and with two other three dimensional video coding methods: discrete cosine transform coding with interframe prediction and intra/

Videoflow: A flow-based generative model for video
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To our knowledge, our work is the first to propose multi frame video prediction with normalizing flows. We describe an approach for modeling the latent space dynamics, and Secondly, for the distribution P(X4|X 4) obtained from each test-set video as explained above, we then randomly sample another video from the test-set and choose it’s 4th frame which we describe

AVS-The Chinese next-generation video coding standard
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The Picture layer provides the coded representation of a video frame . It comprises a header with mandatory and optional parameters and optionally with user data. Three types of Motion estimation produces motion vectors used by the motion compensation unit to produce a forward prediction or interpolated prediction for the current frame . Motion vectors are coded for

View synthesis for multiview video compression
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To take advantage of the correlation between views, we compare the performance of disparity compensated view prediction and view synthesis prediction to independent coding of all The latter adds the ability to synthesize a virtual version of the frame to be encoded from previously encoded frames of other cameras and uses the virtual frame as a prediction reference

Video Frame Prediction Using Convolutional LSTM Networks
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This thesis evaluated Convoultional LSTM (ConvLSTM) for frame prediction to help better understand motion in neural networks. Three different neural networks were implemented and trained. The three networks included, the original ConvLSTM paper by Shi et al.[35], the Spatio-Temporal network by Patraucean et al.[32], and the VPN by Kalchbrenner et al.[21]

Video compression
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Motion compensated prediction : Predict the current frame based on reference frame (s) while compensating for the motion Examples of block-based motion-compensated prediction Original goal to support interlaced video from conventional television; Eventually extended to support HDTV

FPGA Implementation of Modified Intra- Frame Prediction for H. 264 Video Codec
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Video communication is a rapidly growing field for many applications, which includes conversational video like video telephony, video conferencing through a wired or wireless medium and streaming video that includes videoon-demand, high-definition television applications. In general H. 264 intra prediction offers nine prediction modes for 4x 8×8

EndoRCN: recurrent convolutional networks for recognition of surgical workflow in cholecystectomy procedure video
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Overall, we postprocessed the video frame by frame from the beginning to the end, by only using the phase predictions of previous frames and the current frame prediction probability. We did not look at subsequent frames to ensure our recognition method performed in the online mode. The last important thing is that, the high-quality results from EndoRCN is crucial for

Predrnn: Recurrent neural networks for predictive learning using spatiotemporal lstms
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as an example, the measurements are the three RGB channels, and the observation at each time step is a 3D video frame of RGB image. Another example is radar-based precipitation Our model is demonstrated to achieve the state-of-the-art performance on three video prediction datasets including both synthetic and natural video sequences. Our PredRNN model isrule, minimizing prediction error on natural video patches leads to receptive fields similar to those found in Macaque monkey visual area V1. I scale this model to video of natural scenes In the case of video the frame to frame differences include translation, rotation, noise, illumination changes, etc. In short all the transformations that we wish to achieve invariance to. A

Video Frame Prediction
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Given a frame or a sequence of frames ( video ) predict the next frame or next sequence of frames It’s an improvement over CDNA net, that aims to sharpen the long-term prediction by introducing latent random variables to code, such that the learnt latent distribution contains guidance on how to predict. The left animations are the original video the right

Compatible video coding of stereoscopic sequences using MPEG-2s scalability and interlaced structure
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Field picture structure allows field predictions and even between fields of the same frame but does not permit frame predictions. Frame picture structure allows either field prediction or frame prediction selected on the macroblock level, but field predictions do not exist between fields of the same frame . Converting two progressive stereo sequences into an interlaced

Concepts and performance of next-generation video compression standardization
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We briefly review some prior performance comparisons before we compare H.264’s intra- frame coding efficiency to the known still image compression standards JPEG, JPEG2000, and Motion JPEG2000. In addition, there are 4 intra- frame prediction modes on a 16 16-block basis, including DC, horizontal, vertical and a so-called plane prediction . Our experiments have shown that, for most HD sequences, block sizes smaller than 8 X8 have little contribution on inter- frame prediction . So AVS1P2 selects 8X8 as the smallest block size, which also can reduce encoding and decoding complexity. The segmentations of the macroblock of AVS1-P2 for motion compensation are illustrated in Fig.2.In this paper, the performance of the pixel domain distributed video co is improved by using better side information based derived by motion compensated frame interpolation the encoder for hybrid video coding are not adequate to perform frame interpolation since they attempt to choose the best prediction for the current frame in the rate-distortion sense. ForUnlike the existing work [ 9 11] which was performed using MPEG-1 or MPEG-2 video traces, we investigate realworld MPEG-4 video traces which have not been studied in dynamic This is not surprising because with single- frame ahead traffic prediction next frame has to be predicted for every frame . Accordingly, the dynamic bandwidth allocation has to beframe using GoogLeNet and then apply a LSTM [20] on the top for feature aggregation for video action recognition. The second category methods explore the use of 3D ConvNets for We sought to analyze a large-scale, real-world video dataset in order to understand challenges in prediction of near-collision events. However, based on our survey, existing datasets had

Intra prediction by template matching
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Intra prediction is an effective method for reducing the coded information of an image or an intra frame within a video sequence. The conventional method today is to create a sample video coding where intra predictive coding and texture synthesis are combined. The method proposed augmented the intra prediction method of the H.264/AVC with sample prediction

A novel macroblock-tree algorithm for high-performance optimization of dependent video coding in H. 264/AVC
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Typically inter prediction is less useful in sections of video with high inter residual, and thus the value of a higher quality reference frame is lower. As such, qcomp adjusts the quality of frames in inverse proportion Note that, if we ignore the effects of interpolation filters for simplicity, at most 4 macroblocks in each frame can be used for prediction of the current macroblock.

Evaluation and comparison of motion estimation algorithms for video compression
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In this paper various computing techniques are evaluated in video compression for achieving global optimal solution for motion estimation. Zero motion prejudgment is implemented for in the frame prediction and also least prediction error in all video sequences. Thus ARPS technique is more efficient than the conventional searching algorithms in video compression.

Hierarchical model for long-term video prediction
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In this paper, we present a hierarchical model of pixellevel video prediction . Using human action video dataset as benchmark, we demonstrate our hierarchical prediction approach that For future work, we will work on making long-term RGB video frame prediction . This work only generates a single feature trajectory and work can be done on generating predictions forFigure 1 shows an overview of the algorithm steps, starting from a video sequence to an output solution. Four groups of steps are highlighted: video frame analysis, construction of the descriptor with autonomous fragments, dimensionality reduction and video classification. Our work is organized in a modular manner, such that its parts can be replaced by some other

IBBB Inter Frame Prediction of H. 264/AVC
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to reduce the size of the dedicated video and specialized high-quality one. If the videos are send or receive, they need a wide bandwidth to capture this amount of information in the The work is focusing in inter frame prediction using the (IBBB) frame pattern. The video that was subjected to encoding and decoding processing was (Xylophone video name) with (

Game Engine Learning from Video .
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capable of learning a game engine, the backend set of rules that runs a game, from input video . First, our system scans each input video frame to determine the set of objects present per One-step time-dependent future video frame prediction with a convolutional encoder-decoder neural network. arXiv preprint arXiv:1702.0412 2017. [Zook and Riedl] Alexander

An open source tracking testbed and evaluation web site
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To validate this assumption, we tested a simplified three- frame computation method (the actual motion prediction module in the testbed uses a sliding window of N previous object centroids to more robustly estimate current velocity). We model the observed displacement of a target from one video frame to the next using two terms (see Figure 3). The first term, d_camera,

Performance Analysis of Rate Control Scheme in H. 264 Video Coding
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In this proposed work, the rate control for the prediction frame done after encoding the I-frames. Here, QP estimation happens between the encoder interface and the user interface. The rate In this work, 4×4 prediction has been implemented. After the prediction of the first frame called I- frame next subsequent frames are predicted ie P-frames called as an Inter Frame

Two-stream convolutional networks for dynamic saliency prediction
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More specifically, we extract temporal information via optical flow between consecutive video frames and investigate different ways to use this additional information in saliency prediction . We model two different two-stream To understand the contribution of temporal information to the saliency prediction by itself, we develop a second single frame baseline. As shown in

Anticipating the future by watching unlabeled video
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Given a video frame xi t at time t from video i, our goal is to predict the visual representation for the future frame xi Feedforward Network: We compare against a network that makes a prediction given only a single frame for K = 1. Overall, our results suggest that our model can be improved by observing several frames, likely because the extra frames provide some motion

End to end video quality prediction for hevc video streaming over packet networks
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the proliferation of video streaming services. This work proposes an end-to-end Video Quality Prediction (VQP) model suitable for newly released HEVC video compression standard. The quality is a function of bitrate (BR) and the content type (CT) as shown in Equation we did not specifically include encoded video frame rate as a parameter in our prediction model.

Comparative study of compression methodologies for digital angiogram video
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In this paper we look at both inter and intra- frame techniques, examining the specific challenges that angiogram video poses in terms of motion compensation, and considering both This section explores the effectiveness of methods in exploiting temporal redundancy through prediction and motion compensation. Figures 4 and 5 show the ratedistortion results for

A CABAC based HEVCc video steganography algorithm without bitrate increase
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The directions of intra- frame prediction are utilized to avert the distortion drift. We also present an information hiding algorithm based on intra- prediction modes and matrix coding for H.264/AVC video stream . Intra-4×4 coded blocks (I4-blocks) are divided into groups and two watermark bits are mapped to every three I4-blocks by matrix coding to map between

Overview of international video coding standards (preceding H. 264/AVC)
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Interframe Motion Prediction â–ª Large areas of images stay the same from frame to frame changing mostly due to motion Half-pel motion compensation (also in MPEG-1) 3-D variable length coding of DCT coefficients Median motion vector prediction More efficient coding pattern signaling () Deletable GOB header overhead (also in MPEG- but not 2)

Intelligent video QoE prediction model for error-prone networks
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This paper explains the need for better QoE prediction models in error-prone networks and the work elucidates the working of PSQA based video quality prediction model to estimate QoE with the usage of Machine learning techniques. VQM model (ITU-T G.1070) finds video quality based on co type, video frame and packet loss. QoE model for HTTP based

An improved selective encryption for H. 264 video based on intra prediction mode scrambling
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With a focus on the widely-used H.264 video coding standard, various encryption algorithms based on the intra prediction mode have been developed while suffering limited scrambling Experimental results have shown that our algorithm can exhibit higher security than existing ones and less impact on the code length and frame rate. Besides it has low computational we have shown a frame from the Foreman video sequence. In this In this book, we will focus on the basic prediction techniques which are used widely in the in modern video codec. Hybrid co structure and inter- and intra- prediction techniques in MPEG- H.264/AVC, and HEVC are discussed together. While we had started our research in theExisting works usually rely on the optical flow estimation to assist other tasks such as action recognition, frame prediction video segmentation, etc. In this paper, we leverage the massive unlabeled video data to learn an accurate explicit motion representation that aligns well with the semantic distribution of the moving objects. Our method subsumes a coarse-tofine

Video Compression Algorithm Using Motion Compensation Technique
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frame and the current frame . The second stage creates the current frame prediction (motion compensation MC), using the motion estimates and the previously reconstructed frame . The This reconstruction process is referred to as To reduce the complexity of intra mode decision, we propose an efficient and fast 4×4 intra prediction mode selection scheme. The proposed

PredCNN: Predictive Learning with Cascade Convolutions.
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We show that PredCNN outperforms the state-ofthe-art recurrent models for video prediction on the standard Moving MNIST dataset and two challenging crowd flow prediction datasets, and Besides one- frame prediction we recursively apply our PredCNN model trained with 4 inputs and 1 target to frame by- frame prediction task and compare it with the other video

Human-explainable features for job candidate screening prediction
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For example, this happens when the vlogger recorded the video in a public space or while on the road. By departing from our face segmented video instead of a whole video frame we minimize the involvement of background in our calculation and thus get a better representation of the subject’s true movement, as we can see in Figure 1. In order to create wMEI, we

Visual interaction networks: Learning a physics simulator from video
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We trained the model to predict a sequence of 8 consecutive unseen future states from 6 frames of input video . Our prediction loss was a normalized weighted sum of the corresponding 8 (a) Mean Euclidean Prediction Error with respect to object distance traveled (measured as a fraction of the framewidth). The VIN is accurate to within 6% after objects have traversed

Keyin: Discovering subgoal structure with keyframe-based video prediction
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In this paper, we introduce a video prediction model that discovers the keyframe structure of image sequences in an unsupervised fashion. We do so using a hierarchical Keyframe- On keyframe discovery, we compare to a frameby- frame sequential prediction baseline that measures the surprise associated with observing a frame given the previous frames andEncoded video streams are transmitted over multiple paths and the reference frames for motion compensated prediction are selected according to the feedback information about the The RPS technique encodes the current video frame with reference to a previous frame selected based on the feedback information instead of always using the last transmitted frame . It can

Error-resilient video compression via multiple state streams
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video compression standards employ an architecture which we refer to as single-state systems since they have a prediction loop with a single state (eg the previous decoded frame ) video into multiple independently decodable streams, each with its own prediction process and state, such that if one stream is lost the other streams can still be used to produce usable

Region-of-interest video compression with a composite and a long-term frame
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Multiple frame prediction has proved successful in increasing the performance of motion compensated video coders. Region-of-interest (ROI) coding in conjunction with a dual frame predictor can lead to interesting design choices on how to allocate the available bitrate among the frame regions. In this work, we tackle the problem of applying ROI coding on aerial