Energy Efficient Data Encryption Techniques in Smartphones

Mujtaba, Ghulam; Tahir, Muhammad; Soomro, Muhammad Hanif

doi:10.1007/s11277-018-5920-1

Cited by 18 publications

(11 citation statements)

References 14 publications

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“…Because of its simplicity and scalability for implementing various configuration devices [21], the proposed approach can be easily adapted to other animated images (WebP), recommendation techniques [25,43], and streaming protocols such as dynamic adaptive streaming over HTTP (DASH). In addition to reducing the required server computational resources, the proposed method can serve as a privacy-preserving solution using efficient encryption techniques [28,30] that can be integrated into other client-driven solutions [26,27]; moreover, it can be adapted in three-screen TV solutions [33,34].…”

Section: Discussionmentioning

confidence: 99%

Client-driven animated GIF generation framework using an acoustic feature

Mujtaba

Lee

Kim

et al. 2021

Multimed Tools Appl

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This paper proposes a novel, lightweight method to generate animated graphical interchange format images (GIFs) using the computational resources of a client device. The method analyzes an acoustic feature from the climax section of an audio file to estimate the timestamp corresponding to the maximum pitch. Further, it processes a small video segment to generate the GIF instead of processing the entire video. This makes the proposed method computationally efficient, unlike baseline approaches that use entire videos to create GIFs. The proposed method retrieves and uses the audio file and video segment so that communication and storage efficiencies are improved in the GIF generation process. Experiments on a set of 16 videos show that the proposed approach is 3.76 times more computationally efficient than a baseline method on an Nvidia Jetson TX2. Additionally, in a qualitative evaluation, the GIFs generated using the proposed method received higher overall ratings compared to those generated by the baseline method. To the best of our knowledge, this is the first technique that uses an acoustic feature in the GIF generation process.

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Section: Discussionmentioning

confidence: 99%

Client-driven animated GIF generation framework using an acoustic feature

Mujtaba

Lee

Kim

et al. 2021

Multimed Tools Appl

Self Cite

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“…Additionally, the proposed method can be adopted in smartphone devices. 28,29 Additionally, the proposed deep neural network can be used for other real-time applications such as vehicle classification. [30][31][32]…”

Section: Discussionmentioning

confidence: 99%

Movie Genre Classification from RGB Movie Poster Image Using Deep Feed-forward Network

Korai

Bouk

Sindhi

2021

Yanbu Journal of Engineering and Science

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A movie poster image is one of the important media in the filmmaking process, providing valuable information about the movie, such as movie titles, characters, and genres. Identifying a movie genre from a poster can be a daunting task, as it can relate to multiple genres. To solve this problem, this paper uses a deep feedforward neural network to classify movie genres from movie poster images. In this regard, we used and trained a state-of-the-art InceptionV3 deep neural network. The network is trained on our dataset consisting of 36,423 movie poster images taken from the IMDB website, which is categorized into 28 genres. The model predicts the top three classes with the highest probability of a particular movie poster.

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“…In addition, this approach can be extended beyond facilitated personalized trailers towards video summarization and highlights. Along with reducing server-side computational resources in process personalized trailers, the proposed client-side approach can support a privacy-preserving solution [13] using efficient encryption techniques [54], [55], can analyze human activity recognition [56], and can be adapted in three screen TV solutions [57], [58].…”

Section: B Discussionmentioning

confidence: 99%

Client-Driven Personalized Trailer Framework Using Thumbnail Containers

Mujtaba

Ryu

2020

IEEE Access

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Movie trailers are prepared using a one-size-fits-all framework. These days, however, streaming platforms seek to overcome this problem and provide personalized trailers via the investigation of centralized server-side solutions. This can be achieved by analyzing personal user data, and can lead to two major issues: privacy violation and enormous demand in computational resources. This paper proposes an innovative, lowpower, client-driven method to facilitate the personalized trailer generation process. It tackles the complex process of detecting personalized actions in real-time from lightweight thumbnail containers. The HTTP live streaming (HLS) server and client are locally configured to validate the proposed method. The system is designed to support a wide range of client hardware with different computational capabilities and has the flexibility to adapt to network conditions. To test the effectiveness of this method, twenty-five broadcast movies, specifically in the western and sports genres, are evaluated. To the best of our knowledge, this is the first-ever client-driven framework that uses thumbnail containers as input to facilitate the trailer generation process.

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Energy Efficient Data Encryption Techniques in Smartphones

Cited by 18 publications

References 14 publications

Client-driven animated GIF generation framework using an acoustic feature

Client-driven animated GIF generation framework using an acoustic feature

Movie Genre Classification from RGB Movie Poster Image Using Deep Feed-forward Network

Client-Driven Personalized Trailer Framework Using Thumbnail Containers

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