Sensor-Assisted Video Encoding for Mobile Devices in Real-World Environments

Chen, Xiaoming; Zhao, Zikai; Rahmati, Ahmad; Wang, Ye; Lin, Zhouchen

doi:10.1109/tcsvt.2011.2114210

Cited by 14 publications

(18 citation statements)

References 32 publications

(29 reference statements)

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“…A built-in three-axis accelerometer was utilized to determine linear displacement on the X-, Y-, and Z-axes. Another method called sensor-assisted video encoding (SaVE) was introduced in [32] to reduce the computational complexity of video encoding. The method calculates the movement of a camera (on mobile devices) and then infers the global motion in a video.…”

Section: Related Workmentioning

confidence: 99%

“…To determine if M1 can accurately represent the global motion between images A and B, an image fusion experiment was designed with Equation (32), where C only represents the image gray value. The image on the left in Figure 14 is the direct fusion result of images A' and B, and the image on the right is the fusion result after a certain translation.…”

Section: Residual Gmementioning

confidence: 99%

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Metadata-Assisted Global Motion Estimation for Medium-Altitude Unmanned Aerial Vehicle Video Applications

Li²,

Ding

et al. 2015

Remote Sensing

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Global motion estimation (GME) is a key technology in unmanned aerial vehicle remote sensing (UAVRS). However, when a UAV's motion and behavior change significantly or the image information is not rich, traditional image-based methods for GME often perform poorly. Introducing bottom metadata can improve precision in a large-scale motion condition and reduce the dependence on unreliable image information. GME is divided into coarse and residual GME through coordinate transformation and based on the study hypotheses. In coarse GME, an auxiliary image is built to convert image matching from a wide baseline condition to a narrow baseline one. In residual GME, a novel information and contrast feature detection algorithm is proposed for big-block matching to maximize the use of reliable image information and ensure that the contents of interest are well estimated. Additionally, an image motion monitor is designed to select the appropriate processing strategy by monitoring the motion scales of translation, rotation, and zoom. A medium-altitude UAV is employed to collect three types of large-scale motion datasets. Peak signal to noise ratio (PSNR) and motion scale are computed. This study's result is encouraging and applicable to other medium-or high-altitude UAVs with a similar system structure.

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Section: Related Workmentioning

confidence: 99%

Section: Residual Gmementioning

confidence: 99%

Metadata-Assisted Global Motion Estimation for Medium-Altitude Unmanned Aerial Vehicle Video Applications

Li²,

Ding

et al. 2015

Remote Sensing

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“…ODERN mobile devices have embraced ultra low-power and low-cost sensors, including accelerometers and gyroscopes [5]. These inbuilt sensors can efficiently provide accurate information regarding the device movement in terms of sensor data.…”

Section: Introductionmentioning

confidence: 99%

“…One of the first successful attempts at sensor assisted global motion estimation for video processing is the Sensor assisted Video Encoding (SaVE) [5]. The SaVE method describes the estimation of angular displacement of a mobile device.…”

Section: Introductionmentioning

confidence: 99%

“…SaVE [5] claims a reduction in complexity by up to 27% 3) The possibility of efficient use of inbuilt sensors, which run continuously in background. However the existing algorithms [5], [6] for motion estimation based on sensor data are restricted to the estimation of angular motion of mobile device. There are no specific algorithms to estimate the linear displacement of mobile device using the inbuilt sensors.…”

Section: Introductionmentioning

confidence: 99%

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Sensor assisted Motion Estimation

Areekath

Palavalasa

2013

2013 Students Conference on Engineering and Systems (SCES)

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Linear displacement estimation of a mobile device is useful in various real time applications, such as Global Motion Estimation (GME) in video encoding, video surveillance etc. Present state of the art techniques perform the global motion estimation using techniques predominantly based on image processing algorithms. These require complex computations and more power consumption.In this paper a novel approach, Sensor assisted Motion Estimation (SaME), to estimate the linear displacement of a mobile device using inbuilt sensors, is proposed. SaME uses inbuilt 3-axis accelerometer to determine the linear displacement along X, Y and Z axes. SaME algorithm is a combination of basic image processing techniques, sensor data and Artificial Intelligence. The Artificial Neural Network (ANN) is used to build the artificial intelligence. ANN is trained to map the sensor data from inbuilt accelerometer to the corresponding displacement data acquired via simple image processing techniques. The resultant model is used to estimate the linear displacement of the mobile device using the sensor data as input. From the results it can be concluded that the inbuilt sensors can be used to arrive at reliable estimates of linear displacement via the proposed method.

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