Real-time video processing using native programming on Android platform

Saipullah, Khairul Muzzammil; Anuar, Ammar; Ismail, Nurul Atiqah; Soo, Yewguan

doi:10.1109/cspa.2012.6194733

Cited by 7 publications

(2 citation statements)

References 9 publications

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“…The structure was proved to be a good tradeoff between programming complexity and processing time and has been already proven to work well in another Android-based experiments [15]. A similar project structure is also used in [13].…”

Section: Implementa On Remarksmentioning

confidence: 99%

Application of the One-factor CIR Interest Rate Model to Catastrophe Bond Pricing under Uncertainty

Nowicki

2014

JAMRIS

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Section: Implementa On Remarksmentioning

confidence: 99%

Application of the One-factor CIR Interest Rate Model to Catastrophe Bond Pricing under Uncertainty

Nowicki

2014

JAMRIS

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“…Rapid usage of video processing has attracted different research group to scientifically prove the effectiveness of this technique (see Table I). In 2012, Saipullah et al [10] researched real-time video processing using native programming to provide a filtering effect on a video file. They implemented image processing methods to each frame of a video captured from a smartphone that was running on an Android platform.…”

Section: Introductionmentioning

confidence: 99%

A New Native Video Filtering based on OpenGL ES for Mobile Platform

Wijayanti

Muktamar

Wibirama

et al. 2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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In the last five years, there have been many Android applications implementing video filter or video effect as an excellent feature. OpenCV is an open source computer vision library that can be simply and easily used for video filtering in Android application. However, using OpenCV library for video filtering commonly yields a bigger size of Android application. The concept of "Develop for Billion People" has enforced the developers to optimize the size of their applications to preserve resources and size of memory-as not all Android devices come with sufficiently large memory. On the other hand, OpenGL ES does not burden the filtering process because of its smaller size when it is implemented during the application development. In this research, we present a new native video processing technique using OpenGL ES. We implement the proposed method on a native video file without decreasing its quality before video filtering process. The experiments were conducted with five different mobile devices. We compared several metrics including: quality of the resulted video, file size of the apk, power consumption, and memory usage. Based on the experimental results, OpenGL ES produces smaller file size of apk (2 MB) compared with the produced file size of apk by OpenCV (20MB). The resulted file after video filtering possesses same properties as observed before video filtering. Additionally, OpenGL ES uses more efficient power with 0.1965 mAh, while OpenCV consumes 0.283 mAh. Finally, video filtering with OpenGL ES uses 29.3% lesser memory than video filtering with OpenCV. The proposed method is proven to be more appropriate with "Develop for Billion People" as it preserves more computational resources compared with the existing video filtering technique in Android.

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