Viewer-Aware Intelligent Efficient Mobile Video Embedded Memory

Chen, Dongliang; Edstrom, Jonathon; Gong, Yifu; Gao, Peng; Yang, Lei; McCourt, Mark E.; Wang, Jinhui

doi:10.1109/tvlsi.2017.2787043

Cited by 10 publications

(8 citation statements)

References 38 publications

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“…Ref. [4][5][6] Ref. [7] General design techniques in order to accommodate the large amount of video data.…”

Section: Different Surroundingsmentioning

confidence: 99%

“…However, these design techniques usually come with significant implementation overhead (e.g., silicon area, delay) to solve failure problems in memories. We have recently explored viewer-aware video memory design by investigating the impact of illuminance levels in different viewing surroundings on the viewer's experience [4,5,6,7], as shown in Fig. 1.…”

Section: Different Surroundingsmentioning

confidence: 99%

“…A vast amount of research has been conducted to improve the power efficiency of video data storage. State-of-the-art, power-efficient video memories consist of either approximate memory with application-level information [8,9,10,11,12] or viewer-aware memories with an awareness of viewer's experience [4,5,6,7]. In this section, some of the existing work related to the proposed technique are briefly reviewed, and the detailed comparison analysis will be provided in Section VII.…”

Section: State Of the Artmentioning

confidence: 99%

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Content-Adaptable ROI-Aware Video Storage for Power-Quality Scalable Mobile Streaming

et al. 2022

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The demand for mobile video streams is constantly increasing. With this demand comes a need for mobile devices to receive more videos at ever increasing quality. However, due to the large size of video data and intensive computational requirements, video streaming requires frequent memory access that consume a substantial amount of mobile device power; as a result, the battery life of mobile devices is limited. In this paper, we present a video content-adaptable Region-of-Interest (ROI)-aware video storage technique that promotes power savings. During the video encoding process on the transmitting server, based on the macroblock variance and ROI characterization, the "macroblocks of interest" are identified and embedded in the encoded bitstream. In the decoding process, a new frame buffer with dynamic power-quality trade-off is presented to adapt to the macroblock characteristics during run-time. Results from the system-level and circuit-level simulations show that the proposed technique enables substantially more truncated bits and significant power savings while delivering similar or better video quality as compared to other state-of-theart solutions.

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“…Ref. [4][5][6] Ref. [7] General design techniques in order to accommodate the large amount of video data.…”

Section: Different Surroundingsmentioning

confidence: 99%

Section: Different Surroundingsmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

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Content-Adaptable ROI-Aware Video Storage for Power-Quality Scalable Mobile Streaming

et al. 2022

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“…Truncation technique is one of the simplest approximation method used in error tolerant applications [9, 10, 24, 25]. In the truncation technique, some LOBs of pixel are neither stored nor processed as errors in LOBs does not perceptually degrade the image/video quality [10]. In this work, two LOBs truncation is used to design an approximate memory for the H.264 video decoder.…”

Section: Truncation Technique For Multimedia Applicationsmentioning

confidence: 99%

“…Multimedia applications are inherently error tolerant where errors can be tolerated in lower order bits (LOBs) of a pixel. This fact is exploited in [3–5, 7–12] to design low power SRAM architecture for H.264 video decoder

normalPSNR = 20 \times \log_{10} \frac{255}{\sqrt{MSE}},

normalMSE = \frac{1}{M N} {false\sum}_{i = 0}^{M - 1} {false\sum}_{j = 0}^{N - 1} [I (i, j) - I_{normalref} (i, j)]^{2},

Peak‐signal‐to‐noise ratio (PSNR) gives the quantitative measure of the relative quality of an image/video with respect to the original error‐free image/video [7] which is defined in (1) and (2), where, MSE is the mean square error calculated based on pixel differences between images stored in approximate and exact memories.…”

Section: Introductionmentioning

confidence: 99%