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

Ding, Xiangling; Yang, Gaobo; Li, Ran; Zhang, Le-Bing; Li, Yue; Sun, Xingming

doi:10.1109/tcsvt.2017.2676162

Cited by 38 publications

(15 citation statements)

References 40 publications

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“…We will further analyze the tampered trace of the resized images with JPEG compression. In addition, since there are still a few image resizing methods [21,22] besides the three methods proposed in this paper, we will attempt to distinguish these image resizing techniques for re-targeted images by applying other multi-class classifiers [23][24][25][26][27][28] and designing more general features from image/video processing methods [29][30][31][32][33][34][35][36][37][38]. In view of the importance of social media digital images in practical applications, research on their authenticity, integrity and traceability has been one of the hot and challenging research topics in the field of information security.…”

Section: Discussionmentioning

confidence: 99%

Detection of Tampering by Image Resizing Using Local Tchebichef Moments

Zhang

Wang

et al. 2019

Applied Sciences

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There are many image resizing techniques, which include scaling, scale-and-stretch, seam carving, and so on. They have their own advantages and are suitable for different application scenarios. Therefore, a universal detection of tampering by image resizing is more practical. By preliminary experiments, we found that no matter which image resizing technique is adopted, it will destroy local texture and spatial correlations among adjacent pixels to some extent. Due to the excellent performance of local Tchebichef moments (LTM) in texture classification, we are motivated to present a detection method of tampering by image resizing using LTM in this paper. The tampered images are obtained by removing the pixels from original images using image resizing (scaling, scale-and-stretch and seam carving). Firstly, the residual is obtained by image pre-processing. Then, the histogram features of LTM are extracted from the residual. Finally, an error-correcting output code strategy is adopted by ensemble learning, which turns a multi-class classification problem into binary classification sub-problems. Experimental results show that the proposed approach can obtain an acceptable detection accuracies for the three content-aware image re-targeting techniques.

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Section: Discussionmentioning

confidence: 99%

Detection of Tampering by Image Resizing Using Local Tchebichef Moments

Zhang

Wang

et al. 2019

Applied Sciences

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“…Analysis of video components along the time dimension is the typical approach to segregating temporally consistent and nonconsistent objects [20], [21]. Numerous approaches to enhancing the quality of video sequences have been proposed, including editing [22], synthesizing [23], [24], resampling [25], [26], and removing certain events and popping artifacts. Sunkavalli et al [27] introduced an editing approach that separates video components into their reflectance, illumination, and geometry factors.…”

Section: Related Workmentioning

confidence: 99%

Frequency-Aware SVD Decomposition and its Application to Color Magnification and Motion Denoising

et al. 2021

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Videos are full of dynamic changes along both the spatial and temporal dimensions. Large, jerky short-term motions make it difficult to extract significant changes from videos such as subtle color changes and long-term motions occurring in time-lapse sequences. In this paper, we introduce two singular value decomposition (SVD)-based video decomposition schemes to clearly reveal such changes. The first scheme involves enhancing the visual characteristics of small subtle color changes in the presence of a wide variety of motion patterns by magnifying their pixel intensities. The second scheme removes short-term motions that visually distract attention from the underlying content of video sequences such as time-lapse videos, snowing scene, and maritime surveillance. Both schemes involve the decomposition of videos into spatiotemporal slices in which each slice is further decomposed into several singular components. The lowrank components that primarily represent background and color intensity information are then temporally processed to magnify the magnitude of the signal at the subtle color change target frequency. At the same time, an approach similar to that used in denoising time-lapse sequences is applied to temporally filter the singular components representing sparse information, thereby removing jittery short-term motions while preserving long-term motions, which are represented by both low-rank and unfiltered sparse components. We demonstrate promising color magnification and motion denoising results that can be obtained much faster than results estimated using state-of-the-art techniques.

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“…Motion-Compensated Frame Interpolation (MCFI) [1] is one of the common temporal-domain tamper operations, and it produces several new video frames between two neighboring video frames along motion trajectories of objects. It can be used to increase the frame rate or temporal resolution of a video sequence, so it is also called Motion-Compensated Frame Rate Upconversion (MC-FRUC) [2,3]. MCFI is a key step for many video applications, e.g., in the low bit-rate video coding, it is used to remove the temporal redundancy [4]; in the slow replay, it is used to improve movement details in a short time interval [5]; in Liquid Crystal Display (LCD), it is used to reduce the hold-type motion blur [6], etc.…”

Section: Introductionmentioning

confidence: 99%

Motion‐Compensated Frame Interpolation Using Cellular Automata‐Based Motion Vector Smoothing

Yin

Sun

et al. 2021

Wireless Communications and Mobile Computing

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Motion-Compensated Frame Interpolation (MCFI) is one of the common temporal-domain tamper operations, and it is used to produce faked video frames for improving the visual qualities of video sequences. The instability of temporal symmetry results in many incorrect Motion Vectors (MVs) for Bidirectional Motion Estimation (BME) in MCFI. The existing Motion Vector Smoothing (MVS) works often oversmooth or revise correct MVs as wrong ones. To overcome this problem, we propose a Cellular Automata-based MVS (CA-MVS) algorithm to smooth the Motion Vector Field (MVF) output by BME. In our work, a cellular automaton is constructed to deduce MV outliers according to a defined local evolution rule. By performing CA-based evolution in a loop iteration, we gradually expose MV outliers and reduce incorrect MVs resulting from oversmoothing as many as possible. Experimental results show the proposed algorithm can improve the accuracy of BME and provide better objective and subjective interpolation qualities when compared with the traditional MVS algorithms.

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

Cited by 38 publications

References 40 publications

Detection of Tampering by Image Resizing Using Local Tchebichef Moments

Detection of Tampering by Image Resizing Using Local Tchebichef Moments

Frequency-Aware SVD Decomposition and its Application to Color Magnification and Motion Denoising

Motion‐Compensated Frame Interpolation Using Cellular Automata‐Based Motion Vector Smoothing

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