Qualitative Assessment of Video Stabilization and Mosaicking Systems

Zhang, Chao; Chockalingam, Prakash; Kumar, Abhimanyu; Burt, Peter J.; Lakshmikumar, Arvind

doi:10.1109/wacv.2008.4544008

Cited by 8 publications

(7 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Liu et al [6] suggested an empirical process that assumes that if the more energy is in the low-frequency part of a video, the video will be more stable. We see that most existing proposed metrics [1,2,21,25,26] for VS approaches are either synthetically shaking video-based or heuristic approaches [2,6,24,27].…”

Section: Related Workmentioning

confidence: 96%

“…Qu et al [25] tested their proposed method based on synthetically shaking video to measure the accuracy of the three stabilization algorithms of Deshaker, Grundmann, and Qu. Zhang et al [1] assessed the stability of intensity-based video stabilization and featurebased video stabilization. From the ground truth motion and the estimated motion, the researchers computed a mean square error (MSE) between consecutive frames and root mean square (RMS) error for the whole sequences.…”

Section: Related Workmentioning

confidence: 99%

“…The demand for video stabilization (VS) is increasing over time [1]. Now many video processing software programs and camcorders are using a video stabilization facility [2].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A novel accuracy assessment model for video stabilization approaches based on background motion

Hossain¹,

Nguyen²,

Huh³

2019

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

In this paper, we propose a new accuracy measurement model for the video stabilization method based on background motion that can accurately measure the performance of the video stabilization algorithm. Undesired residual motion present in the video can quantitatively be measured by the pixel by pixel background motion displacement between two consecutive background frames. First of all, foregrounds are removed from a stabilized video, and then we find the two-dimensional flow vectors for each pixel separately between two consecutive background frames. After that, we calculate a Euclidean distance between these two flow vectors for each pixel one by one, which is regarded as a displacement of each pixel. Then a total Euclidean distance of each frame is averaged to get a mean displacement for each pixel, which is called mean displacement error, and finally we calculate the average mean displacement error. Our experimental results show the effectiveness of our proposed method.

show abstract

Section: Related Workmentioning

confidence: 96%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A novel accuracy assessment model for video stabilization approaches based on background motion

Hossain¹,

Nguyen²,

Huh³

2019

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

show abstract

“…ITF is the PSNR between two consecutive stabilized frames in the cases without ground-truth videos, while GTF is the PSNR between the ground-truth frame and the current stabilized frame. Zhang et al [4] proposed some performance metrics for electronic stabilization, such as accuracy, capture range, tolerance, etc.…”

Section: Introductionmentioning

confidence: 99%

“…Offiah et al [5] proposed a subjective Mean Opinion Score (MOS) metric by extending Zhang et al's work [4]. They evaluated the performance of four stabilization algorithms in endoscopy by comparing the stabilized videos to the synthetic reference version based on both the objective and subjective metrics.…”

Section: Introductionmentioning

confidence: 99%

Shaking video synthesis for video stabilization performance assessment

Song

Xue

2013

2013 Visual Communications and Image Processing (VCIP)

View full text Add to dashboard Cite

The goal of video stabilization is to remove the unwanted camera motion and obtain stable versions. Theoretically, a good stabilization algorithm should remove the unwanted motion without the loss of image qualities. However, due to the lack of ground-truth video frames, the accurate performance evaluation of different algorithms is hard. Most existing evaluation techniques usually synthesize stable videos from shaking ones, but they are not effective enough. Different from previous methods, in this paper we propose a novel method which synthesize shaking videos from stable frames. Based on the synthetic shaking videos, we perform preliminary video stabilization performance assessment on three stabilization algorithms. Our shaking video synthesis method can not only give a benchmark for full-reference video stabilization performance assessment, but also provide a basis for exploring the theoretical bound of video stabilization which may help to improve existing stabilization algorithms.

show abstract

A Novel Performance Metric for the Video Stabilization Method

Hossain

Huh

2019

Advances in Intelligent Systems and Computing

View full text Add to dashboard Cite

Qualitative Assessment of Video Stabilization and Mosaicking Systems

Cited by 8 publications

References 6 publications

A novel accuracy assessment model for video stabilization approaches based on background motion

A novel accuracy assessment model for video stabilization approaches based on background motion

Shaking video synthesis for video stabilization performance assessment

A Novel Performance Metric for the Video Stabilization Method

Contact Info

Product

Resources

About