Fast Local Motion Estimation and Robust Global Motion Decision for Digital Image Stabilization

Kuo, Tien-Ying; Wang, Chung-Hsin

doi:10.1109/iih-msp.2008.232

Cited by 5 publications

(5 citation statements)

References 6 publications

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“…For example, if NTB is set to 5, this means that only the most significant 3 bit-planes will be used in matching criterion computation. In [18], a single bit-plane was generated using four bit-planes by placing them in regular order and NNMP was utilized as the matching criterion for this method. This approach was designed for global motion estimation and is not suitable for local motion estimation.…”

Section: Low Bit-depth-based Motion Estimation Approachesmentioning

confidence: 99%

“…In [16], a combination of the methods proposed in [15] and [18] was presented where the most significant three bit-planes are placed as in Figure 2 to construct the binary image. In this way, strong parts of these approaches are used together to improve ME accuracy and reduce computational complexity.…”

Section: Low Bit-depth-based Motion Estimation Approachesmentioning

confidence: 99%

“…In this way, strong parts of these approaches are used together to improve ME accuracy and reduce computational complexity. It is important to note that during the NNMP computation in [16] and [18], the same selection approach is applied to all candidate locations independently.…”

Section: Low Bit-depth-based Motion Estimation Approachesmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive bit-plane selection-based low complexity motion estimation for screen content coding

Duvar¹,

Urhan²

2018

Turk J Elec Eng & Comp Sci

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Screen content video coding has become an emerging research topic with the spread of applications such as cloud gaming, screen/desktop virtualization, and mobile or external display interfacing. Screen content videos have different features compared to conventional camcorder-captured scenes. In this work, a novel low bit-depth representation-based motion estimation approach is proposed to exploit screen content specific features to improve coding efficiency. The proposed approach is based on an adaptive selection of Gray-coded bit-planes in order to generate low bit-depth representation of original screen content frames. The experimental results show that the motion estimation performance of the proposed approach is significantly better compared to the methods in the same category for screen content videos.

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Section: Low Bit-depth-based Motion Estimation Approachesmentioning

confidence: 99%

Section: Low Bit-depth-based Motion Estimation Approachesmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive bit-plane selection-based low complexity motion estimation for screen content coding

Duvar¹,

Urhan²

2018

Turk J Elec Eng & Comp Sci

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“…Therefore, it is important to design an algorithm specially for estimating TMV to improve the MCFI [2]- [7] quality. Furthermore, the accuracy of TMV can affect other kinds of video applications, such as error concealment [9], object tracking [8], [9], shot change detection [10], image stabilization system [11] and slow motion replay.…”

Section: Introductionmentioning

confidence: 99%

True motion estimation based on reliable motion decision

Kuo

Hsieh

et al. 2013

2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference

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This paper presents a novel technique to estimate true motion vectors (TMVs) during video encoding. The proposed method designs a system unit to collect motion vectors' (MVs') characteristic in the video encoder. MVs are then classified into reliable and unreliable MVs based on the collected early-stop distribution of each block. The reliable MVs are directly assigned as TMVs, whereas the unreliable MVs must go through the refinement processing for TMV conversion. This algorithm can be easily integrated into the existing video encoders. The experiment result shows that proposed method is superior to the literature works in terms of either performance or computation complexity.

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“…[4][5][6] In these circumstances the objects within a scene are the sole cause of the pixel motion; however, in airborne imaging applications, the camera motion compounds the scene with pixel motion. The latter has been rarely reported in the literature; although 7 reports image registration from an aerial platform for traffic monitoring and 8 reports global motion for object detection.…”

Section: Introductionmentioning

confidence: 99%

Global registration and moving objects detection in noisy airborne image sequences

et al. 2010

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This paper presents a method for registration of noisy airborne images for the purpose of the detection of moving objects. A new iterative algorithm is developed and presented for the correction of geometrical distortion caused by global motion in a scene. A binary hypotheses test is subsequently established using a likelihood ratio test (LRT) to classify the pixels in the corrected image as either locally moving (object motion) or not moving (stationary). The paper also incorporates the use of the Expectation-Maximization method for estimation of statistical image features needed by the LRT. We use and present experiments with real image sequences to validate the analytical developments. C 2010 Society of Photo-Optical Instrumentation Engineers.

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Fast Local Motion Estimation and Robust Global Motion Decision for Digital Image Stabilization

Cited by 5 publications

References 6 publications

Adaptive bit-plane selection-based low complexity motion estimation for screen content coding

Adaptive bit-plane selection-based low complexity motion estimation for screen content coding

True motion estimation based on reliable motion decision

Global registration and moving objects detection in noisy airborne image sequences

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