Showing papers on "Residual frame published in 2018"

Less is more: Micro-expression recognition from video using apex frame

Abstract: Despite recent interest and advances in facial micro-expression research, there is still plenty of room for improvement in terms of micro-expression recognition. Conventional feature extraction approaches for micro-expression video consider either the whole video sequence or a part of it, for representation. However, with the high-speed video capture of micro-expressions (100–200 fps), are all frames necessary to provide a sufficiently meaningful representation? Is the luxury of data a bane to accurate recognition? A novel proposition is presented in this paper, whereby we utilize only two images per video, namely, the apex frame and the onset frame. The apex frame of a video contains the highest intensity of expression changes among all frames, while the onset is the perfect choice of a reference frame with neutral expression. A new feature extractor, Bi-Weighted Oriented Optical Flow (Bi-WOOF) is proposed to encode essential expressiveness of the apex frame. We evaluated the proposed method on five micro-expression databases—CAS(ME) 2 , CASME II, SMIC-HS, SMIC-NIR and SMIC-VIS. Our experiments lend credence to our hypothesis, with our proposed technique achieving a state-of-the-art F1-score recognition performance of 0.61 and 0.62 in the high frame rate CASME II and SMIC-HS databases respectively.

Identification of Motion-Compensated Frame Rate Up-Conversion Based on Residual Signals

Abstract: Motion-compensated frame rate up-conversion (MC-FRUC) is originally presented to increase the motion continuity of low frame rate videos by periodically inserting new frames, which improves the viewing experience. However, MC-FRUC can also be exploited to fake high frame rate videos or splice two videos with different frame rates for malicious purposes. A blind forensics approach is proposed for the identification of various MC-FRUC techniques. A theoretical model is first built for residual signal, which is exploited as tampering trace for blind forensics. The identification of various MC-FRUC techniques is then converted into a problem of discriminating the differences of residual signals among them. A pre-classifier is designed to suppress the side effects of original frames and static interpolated frames in candidate videos. Then, spatial and temporal Markov statistics features are extracted from the residual signals inside the interpolated frames for MC-FRUC identification. Five open MC-FRUC softwares and six representative MC-FRUC techniques have been tested, and experimental results show that the proposed approach can effectively locate interpolated frames and further identify the adopted MC-FRUC technique for both uncompressed videos and compressed videos with high perceptual qualities.

Authentication of Surveillance Videos: Detecting Frame Duplication Based on Residual Frame.

Abstract: Nowadays, surveillance systems are used to control crimes. Therefore, the authenticity of digital video increases the accuracy of deciding to admit the digital video as legal evidence or not. Inter-frame duplication forgery is the most common type of video forgery methods. However, many existing methods have been proposed for detecting this type of forgery and these methods require high computational time and impractical. In this study, we propose an efficient inter-frame duplication detection algorithm based on standard deviation of residual frames. Standard deviation of residual frame is applied to select some frames and ignore others, which represent a static scene. Then, the entropy of discrete cosine transform coefficients is calculated for each selected residual frame to represent its discriminating feature. Duplicated frames are then detected exactly using subsequence feature analysis. The experimental results demonstrated that the proposed method is effective to identify inter-frame duplication forgery with localization and acceptable running time.

Pipelined video decoder system

Abstract: Efficient decoding of video content that may involve intra block copy operations, such as copying pixel data from one region of a frame to another region of the same frame is described. For example, a method to decode the video content may involve identifying the video frame in which intra block copy operation is to be performed, prior to the intra block copy operation being initiated. A video decoder may prefetch the pixel data from the source region to a local buffer with low memory latency such that the source pixel data to be copied into the destination blocks in the video frame is readily available. Thus, costly, and time consuming memory access may be avoided, and in turn a video decoding pipeline may operate smoothly without any stalling.

Long-Term Video Generation with Evolving Residual Video Frames

Abstract: In this paper, we propose a novel long-term video generation algorithm, motivated by the recent developments of unsupervised deep learning techniques. The proposed technique learns two ingredients of internal video representation, i.e., video textures and motions to reproduce realistic pixels in the future video frames. To this aim, the proposed technique uses two encoders comprising convolutional neural networks (CNN) to extract spatiotemporal features from the original video frame and a residual video frame, respectively. The use of the residual frame facilitates the learning with fewer parameters as there are high spatiotemporal correlations in a video. Moreover, the residual frames are efficiently used for evolving pixel differences in the future frame. In a decoder, the future frame is generated by transforming the combination of two feature vectors into the original video size. Experimental results demonstrate that the proposed technique provides more robust and accurate results of long-term video generation than conventional techniques.

Real phase retrieval from unordered partial frame coefficients

Abstract: We study the signal recovery from unordered partial phaseless frame coefficients To this end, we introduce the concepts of m-erasure (almost) phase retrievable frames We show that with an m-erasure (almost) phase retrievable frame, it is possible to reconstruct (almost) all n-dimensional real signals up to a sign from their arbitrary N − m unordered phaseless frame coefficients, where N stands for the element number of the frame We give necessary and sufficient conditions for a frame to be m-erasure (almost) phase retrievable Moreover, we give an explicit construction of such frames based on prime numbers

A Double Background Based Coding Scheme for Surveillance Videos

Abstract: The rapid growth of surveillance videos poses a huge challenge for video coding technology To make the best use of the special characteristics of surveillance videos, the prediction and coding methods with high-quality background picture (BG-picture) has been proposed However, a large number of frames are always required for training in traditional background modeling methods, and too many bits are spent to encode the BG-picture Therefore, we propose a double background based coding scheme for surveillance videos, in which two background frames are generated from the reconstructed frames and original frames respectively Then residual frame between the original background and the reconstructed background is encoded to reduce the bit cost of the BG-picture The experiments on surveillance videos shows that compared with HM140, the proposed method can achieve about 17 percent bit rate saving on average Up to 40 percent bit rate saving can be observed on surveillance videos

An Approach to Parallel Transformation Technique for High-Efficiency Video Coding

Abstract: Compression plays a vital role in video processing. The reduction or removal of redundant data from raw video stream makes an effective video file transmission and storage with High Efficiency Video Coding (HEVC). HEVC is the standard developed by the Joint Collaborative Team on Video Coding (JCT-VC). HEVC or H.265 includes several modifications compared with its predecessor the H.264. During the HEVC encoding process, HEVC uses transform coding on self-contained and inter-predicted frame residuals, which possess distinct characters compared to residual frame. The residual frame information is performed using transformation, quantization, and entropy coder, and the encoded bit stream is decoded to reconstruct the original video. In this work, by using parallel transformation technique, the residual frame encoding and decoding is implemented for the improvement of HEVC. The results are presented with the use of proposed parallel transformation technique in the framework of the HEVC and performance results of our implementation show better results in video quality metrics. Our proposed implementation on HEVC transform, scaling, and quantization carried out on various video frame formats has shown better results compared to conventional discrete wavelet transform.

Unidirectional distributed video decoding method based on iterative correlation noise refinement

Abstract: The invention discloses a unidirectional distributed video decoding method based on iterative correlation noise refinement, and the specific steps are as follows: (1) performing discrete cosine transform on a block frame, quantizing and extracting a bit plane; (2) estimating an encoding code rate; (3) performing LDPCA encoding on the bit plane; (4) performing discrete cosine transform on the sideinformation frame and the motion compensated residual frame, and extracting the bit plane; (5) estimating the correlation noise; (6) performing LDPCA decoding on the bit plane to be decoded; (7) refining the residual coefficient value by using the updated reconstruction coefficient value; and (8) obtaining the final reconstructed WZ frame by inverse discrete cosine transform. Based on the iterative decoding, the invention updates the correlation noise distribution by updating the reconstructed coefficients obtained by the last decoding to refine the residual coefficients. The invention improves the correlation noise estimation precision and the reconstruction quality, and solves the problem that the reconstruction quality is degraded due to under-estimation of the code rate on the codingend.