東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

Turning Visual Noise into Hardware Efficiency: Systems of Viewer and Content Aware Power-Quality Scalable Embedded Memories with Ecc-Adaptation for Big Videos and Deep Learning.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Turning Visual Noise into Hardware Efficiency: Systems of Viewer and Content Aware Power-Quality Scalable Embedded Memories with Ecc-Adaptation for Big Videos and Deep Learning./
作者:	Haidous, Ali Ahmad.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	191 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
Contained By:	Dissertations Abstracts International83-03B.
標題:	Computer engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28649502
ISBN:	9798538105915

Turning Visual Noise into Hardware Efficiency: Systems of Viewer and Content Aware Power-Quality Scalable Embedded Memories with Ecc-Adaptation for Big Videos and Deep Learning.
Haidous, Ali Ahmad.

Turning Visual Noise into Hardware Efficiency: Systems of Viewer and Content Aware Power-Quality Scalable Embedded Memories with Ecc-Adaptation for Big Videos and Deep Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 191 p.

Source: Dissertations Abstracts International, Volume: 83-03, Section: B.

Thesis (Ph.D.)--North Dakota State University, 2021.

This item must not be sold to any third party vendors.

Mobile devices, such as smart phones, are being increasingly utilized for watching videos. Video processing requires frequent memory access that consume a significant amount of power due to large data size and intensive computational requirements. This limits battery life and frustrates users. Memory designers are focused on hardware-level power-optimization techniques without consideration of how hardware performance influences viewers' actual experience. The human visual system is limited in its ability to detect subtle degradations in image quality. For example, under conditions of high ambient illumination - such as outdoors in direct sunlight - the veiling luminance (i.e., glare) on the screen of a mobile device can effectively mask imperfections in the image. Under these circumstances, a video can be rendered in lower than full quality without the viewer being able to detect any difference in quality. As a result, the isolation between hardware design and viewer experience significantly increases hardware implementation overhead and power consumption due to overly pessimistic design margins, while integrating the two would have the opposite effect.In this dissertation, viewer-awareness, content-awareness, and hardware adaptation are integrated to achieve power optimization without degrading video quality, as perceived by users. Specifically, this dissertation will (i) experimentally and mathematically connect viewer experience, ambient illuminance, and memory performance; (ii) develop energy-quality adaptive hardware that can adjust memory usage based on ambient luminance to reduce power usage without impacting viewer experience; (iii) design various mobile video systems to fully evaluate the effectiveness of the developed methodologies; and (iv) provide an overview of bleeding edge related area research then push the boundary further using the novel techniques discussed to achieve optimized quality, silicone area overhead, and power reduction in video memory.

ISBN: 9798538105915Subjects--Topical Terms:

621879
Computer engineering.
Subjects--Index Terms:

Big Videos

Turning Visual Noise into Hardware Efficiency: Systems of Viewer and Content Aware Power-Quality Scalable Embedded Memories with Ecc-Adaptation for Big Videos and Deep Learning.
LDR:03269nmm a2200385 4500 001 2345021
005 20220531062337.5
008 241004s2021 ||||||||||||||||| ||eng d
020 $a 9798538105915
035 $a (MiAaPQ)AAI28649502
035 $a AAI28649502
040 $a MiAaPQ $c MiAaPQ
100 1 $a Haidous, Ali Ahmad. $0 (orcid)0000-0002-9799-0631 $3 3683876
245 1 0 $a Turning Visual Noise into Hardware Efficiency: Systems of Viewer and Content Aware Power-Quality Scalable Embedded Memories with Ecc-Adaptation for Big Videos and Deep Learning.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 191 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
500 $a Advisor: Wang, Danling.
502 $a Thesis (Ph.D.)--North Dakota State University, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Mobile devices, such as smart phones, are being increasingly utilized for watching videos. Video processing requires frequent memory access that consume a significant amount of power due to large data size and intensive computational requirements. This limits battery life and frustrates users. Memory designers are focused on hardware-level power-optimization techniques without consideration of how hardware performance influences viewers' actual experience. The human visual system is limited in its ability to detect subtle degradations in image quality. For example, under conditions of high ambient illumination - such as outdoors in direct sunlight - the veiling luminance (i.e., glare) on the screen of a mobile device can effectively mask imperfections in the image. Under these circumstances, a video can be rendered in lower than full quality without the viewer being able to detect any difference in quality. As a result, the isolation between hardware design and viewer experience significantly increases hardware implementation overhead and power consumption due to overly pessimistic design margins, while integrating the two would have the opposite effect.In this dissertation, viewer-awareness, content-awareness, and hardware adaptation are integrated to achieve power optimization without degrading video quality, as perceived by users. Specifically, this dissertation will (i) experimentally and mathematically connect viewer experience, ambient illuminance, and memory performance; (ii) develop energy-quality adaptive hardware that can adjust memory usage based on ambient luminance to reduce power usage without impacting viewer experience; (iii) design various mobile video systems to fully evaluate the effectiveness of the developed methodologies; and (iv) provide an overview of bleeding edge related area research then push the boundary further using the novel techniques discussed to achieve optimized quality, silicone area overhead, and power reduction in video memory.
590 $a School code: 0157.
650 4 $a Computer engineering. $3 621879
650 4 $a Electrical engineering. $3 649834
650 4 $a Engineering. $3 586835
650 4 $a Silicon. $3 669429
650 4 $a Software. $2 gtt. $3 619355
650 4 $a Random access memory. $3 623617
650 4 $a Metadata. $3 590006
650 4 $a Failure. $3 3561225
650 4 $a Deep learning. $3 3554982
650 4 $a Semiconductors. $3 516162
650 4 $a Video recordings. $3 575241
650 4 $a Optimization techniques. $3 3681622
650 4 $a Adaptation. $3 3562958
650 4 $a Internet of Things. $3 3538511
650 4 $a Data analysis. $2 bisacsh $3 3515250
650 4 $a Green buildings. $3 3559500
650 4 $a Confidence intervals. $3 566017
650 4 $a Designers. $3 589292
650 4 $a Error correction & detection. $3 3480646
650 4 $a Signal to noise ratio. $3 3681178
650 4 $a Power. $3 518736
650 4 $a Neural networks. $3 677449
650 4 $a Design. $3 518875
650 4 $a Energy efficiency. $3 3555643
650 4 $a Streaming media. $3 3564417
650 4 $a Decision trees. $3 827433
653 $a Big Videos
653 $a Content Aware
653 $a Embedded Memory
653 $a Low Power
653 $a Viewer Aware
690 $a 0464
690 $a 0544
690 $a 0537
690 $a 0389
710 2 $a North Dakota State University. $b Electrical and Computer Engineering. $3 2095311
773 0 $t Dissertations Abstracts International $g 83-03B.
790 $a 0157
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28649502