1.15 Mbit/s coding of video signals including global motion compensation

Adolph, D.; Buschmann, Ralf

doi:10.1016/0923-5965(91)90014-s

Cited by 29 publications

(20 citation statements)

References 8 publications

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“…13 and 14, where the only variation in the video is due to camera translation. On the other hand, if there is additional object movement in the scene such as in PingZoom and Ping-Pan, then our edge-based technique does as well as any global MC technique such as 2). An example of this is shown in the plot of Fig.…”

Section: Fixed Point Implementationmentioning

confidence: 99%

“…A5 mentioned earlier, there are a number of existing zoom and pan detection algorithms [2], [3], [23], most of which consist of two steps: 1) estimate local motion vector for each block; 2) use the location of blocks in the current frame and their corresponding motion compensated location in the next frame in (5) in order to find the linear least-squares estimate of zoom and pan parameters [3]. Since zoom and pan are smoothly varying processes, in many practical situations it is sufficient to use the motion vectors between every few frames in finding the least-squares estimate of zoom and pan parameters.…”

Section: A Zoom Pan and Rotation Algorithmmentioning

confidence: 99%

“…In addition to [2], Baker [3] has shown that a two-stage global/local motion compensation approach improves motion prediction and reduces the amount of motion side information. Keesman [4], Hoetter [5], and Wu and Kittler [6] also show the advantages of global motion estimation schemes.…”

Section: Introduction Otion Compensation (Mc) Plays An Importantmentioning

confidence: 99%

“…Even though global motion estimation is not officially part of these two standards, several authors have exploited camera motion estimation in the context of video sequence coding. For example, Adolph and Buschmann [2] propose a coder for television video signals at 1.15 Mbps, where global MC is carried out before local one. In doing so, they assume the global motion to consist of only zoom and pan, and estimate it using a frame-matching algorithm.…”

Section: Introduction Otion Compensation (Mc) Plays An Importantmentioning

confidence: 99%

“…The techniques used for estimating the global motion parameters vary considerably. For instance [4]- [6] use pel recursive algorithms, while [2], [3] use luminance block matching.…”

Section: Introduction Otion Compensation (Mc) Plays An Importantmentioning

confidence: 99%

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Edge-based 3-D camera motion estimation with application to video coding

Zakhor

Lari

1993

IEEE Trans. on Image Process.

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Abstract-The evolution of an image sequence obtained by a real camera from a real scene can be conceptually separated into two parts: 1) motion of the camera and 2) motion of the objects in a scene. Most existing motion estimation algorithms use the block matching algorithm (BMA) to model both the camera motion and local motion due to the objects. In doing so, successive frames are divided into small blocks and the movement of each block is approximately modeled by a translation, thus resulting in one motion vector per block. In this paper, we propose two classes of algorithms for modeling camera motion in video sequences captured by a camera. The first class can be applied in situations where there is no camera translation and the motion of camera can be adequately modeled by zoom, pan, and rotation parameters. The second class is more general in that it can be applied to situations where the camera is undergoing a translational motion, as well as a rotation and zoom and pan. This class uses seven parameters to describe the motion of the camera and requires the depth map to be known at the receiver. The salient feature of both of our algorithms is that the camera motion is estimated using binary matching of the edges in successive frames. In doing so, we show that unlike local motion estimation, edge matching can be sufficient in estimating camera motion parameters. Finally, we compare the rate distortion characteristics of our algorithms with that of the B M A and show that we can achieve similar performance characteristics as BMA, with reduced computational complexity.

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Section: Fixed Point Implementationmentioning

confidence: 99%

Section: A Zoom Pan and Rotation Algorithmmentioning

confidence: 99%

Section: Introduction Otion Compensation (Mc) Plays An Importantmentioning

confidence: 99%

Section: Introduction Otion Compensation (Mc) Plays An Importantmentioning

confidence: 99%

“…The techniques used for estimating the global motion parameters vary considerably. For instance [4]- [6] use pel recursive algorithms, while [2], [3] use luminance block matching.…”

Section: Introduction Otion Compensation (Mc) Plays An Importantmentioning

confidence: 99%

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Edge-based 3-D camera motion estimation with application to video coding

Zakhor

Lari

1993

IEEE Trans. on Image Process.

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Motion-Adaptive Modelling of Scene Content for Very Low Bit Rate Model-Assisted Coding of Video

Rabiner

Jacquin²

1997

Journal of Visual Communication and Image Representation

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opposed to performing ''blind encoding'' of pixel values or data blocks. This assumption was at the core of the We describe a system which detects and tracks types of objects of interest specified a priori, such as human faces and concept of model-assisted coding of video teleconferencing bodies, in video sequences. Face location tracking algorithms sequences described in [1,2], in which finer quantization described in previous documents are extended to enable robust (requiring the allocation of a higher coding rate) is perand accurate tracking of faces and bodies in video scenes with formed in previously identified areas of interest, such as complex spatio-temporal backgrounds, i.e. cluttered static back-human faces. grounds and moving backgrounds (for example, due to cameraThe validity of this assumption is further demonstrated motion or zoom). The new tracking algorithm includes backin the work described in this paper. Here, additional knowlground removal obtained from global motion estimation (GME), edge about sequence content in the form of global backas well as the use of combined motion and edge data and knowlground motion estimation is obtained and used to perform edge-based temporal adaptation, which jointly add significant background removal for more robust tracking of human robustness to the tracking. For typical ''head-and-shoulders'' video material with up to two persons in the scene and a faces and bodies. The added robustness is especially sigstill background, an additional 24% of successful tracking is nificant in cases of complex spatio-temporal scene backachieved by the proposed algorithm, bringing the average suc-grounds, which would typically occur with video data access rate to about 96%. For more complex material with moving quired from a hand-held video camera (e.g., in a mobile backgrounds, successful face and body tracking is achieved at situation). Many foreground/background segmentation an average rate of about 86%, whereas an algorithm which techniques have been proposed in the literature, initially does not perform background removal could only achieve less in the relatively simple case of stationary (still) backthan 10% of successful tracking. Initial coding experiments grounds [3,4], then in the more general case of moving using the information obtained from face tracking for modelbackgrounds [5][6][7][8][9]. In the former case, the techniques are assisted coding of video in QCIF format at 16 kbps demonstrate typically based on frame differencing, followed by thresh-

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Segmentation-Based Motion Estimation for Second Generation Video Coding Techniques

Dufaux

Moscheni

1996

Video Coding

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1.15 Mbit/s coding of video signals including global motion compensation

Cited by 29 publications

References 8 publications

Edge-based 3-D camera motion estimation with application to video coding

Edge-based 3-D camera motion estimation with application to video coding

Motion-Adaptive Modelling of Scene Content for Very Low Bit Rate Model-Assisted Coding of Video

Segmentation-Based Motion Estimation for Second Generation Video Coding Techniques

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