Rate-Quality Optimized Video Coding

Saw, Yoo-Sok

doi:10.1007/978-1-4615-5125-6

Cited by 13 publications

(6 citation statements)

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“…2 System architecture of the optimized rate control medical video [9]: streaming system. Bo(t)~Fs(t) -(Cr(t) / Fr (t)) -58000 6 Bo(t)Bs+ Bo(t -1) (7) Where Fs(t) and Bo(t-]) are the frame size at time t and the l buffer occupancy at time (t-]) respectively.…”

Section: Cr -Channel Ratementioning

confidence: 99%

Medical Quality of Service for Wireless Ultrasound Streaming in Robotic Tele-Ultrasonography System

Philip¹,

Istepanian²

2007

2007 IEEE International Conference on Networking, Sensing and Control

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presented in this paper. Abstract Mobile healthcare (m-health) is an emerging Generally providing end-to-end quality-of-service (QoS) to research area. There are two critical issues facing successful real time video delivery in wireless networks is becoming an deployment of m-health applications from the wireless increasingly important application requirement in mobile communications perspectives. First, wireless connectivity issues communications and multimedia research. Supporting robust and mobility requirements of real-time bandwidth demanding mhealth applications. Second, are the Quality of Service (QoS) video commations ov wi netors is ansgificant issues from the healthcare perspective and their required levels problem, primarily because of two factors: low bandwidth and to guarantee robust and clinically acceptable healthcare services. the time varying error characteristics of the transmission In this paper, we consider the concept of medical QoS (m-QoS) channel. Therefore, adaptation scheme has to be applied in issues for typical bandwidth demanding m-health application order to overcome these challenges. (tele-ultrasound streaming) in 3G environments. Specifically we For bandwidth demanding i-health applications, we adopt introduce this new concept of m-QoS that provide a sub-category of quality of services from the m-health perspective. To validate here a wireless robotic tele-ultrasonography system OTELO this concept we also propose a new optimal rate control policy as an example to validate the concept presented in this paper based on Q-learning approach to deliver the ultrasound images [2]. OTELO can be considered as bandwidth-demanding over 3G environment. medical data traffic system with different m-QoS requirements. The advanced medical robotic system -OTELO (mObile-Tele-Echography using an ultra-Light rObot) was a Iommundexatios-QoS,aTeyemhedi ml i European IST funded project that developed a fully integrated end-to-end mobile tele-echography system for population groups that are not served locally, either temporarily or I. INTRODUCTION permanently, by medical ultrasound experts. It comprises a n / -Health can be defined as 'mobile computing, fully portable tele-operated robot allowing a specialist N/I -Health can be defined as 'mobile computing, sonographer to perform a real-time robotised tele-echography .IYL.multimedia, medical sensor, and communications1 multimedia medical sensor, and communications to remote patients. Figure I shows the main operational blocks technologies for healthcare' [1]. This emerging concept of the system over 3G communication network. Further represents the evolution of e-health systems from traditional . o . desktop 'telemedicine' platforms to wireless and mobile els of tes configurations. Current and emerging developments in wireless communications integrated with developments in pervasive and wearable technologies will have a radical Exetd atationCommUni cati A Patient Station impact on future healthcare delivery systems. Two important technical issues are crit...

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Section: Cr -Channel Ratementioning

confidence: 99%

Medical Quality of Service for Wireless Ultrasound Streaming in Robotic Tele-Ultrasonography System

Philip¹,

Istepanian²

2007

2007 IEEE International Conference on Networking, Sensing and Control

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“…However, if a scene change occurs, information obtained from previously coded pictures is no more useful and even can cause visual quality degradation in the pictures following the scene change [31]. In [32], Saw et al classify scene variations into different categories, including general scenes, rapid motion, panning, zooming, and scene cuts, etc.In [32], the variance between two pictures is used to detect the scene change. In [31], the signed difference of mean absolute difference (MAD) is used to detect the scene change.…”

Section: Scene Variation and Scene Change Detectionmentioning

confidence: 99%

Rate adaptation transcoding for precoded video streams

Lei

Georganas

2002

Proceedings of the Tenth ACM International Conference on Multimedia

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In order to transmit pre-encoded digital video over heterogeneous networks, it is necessary to employ transcoding techniques that convert pre-encoded video streams into streams having different bit rates and quality. The specified problem is referred to as rate shaping or rate adaptation. In this work, we propose a new rate control scheme for H.263+ based video transcoding. The proposed rate control scheme is comprised of Frame-Layer bit allocation and Macroblock-Layer rate control. At the frame layer, scene context statistics from the incoming video stream are utilized to detect scene changes and determine frame type. The bit budget is allocated to frames according to their energy and frame types. At the macroblock layer, a novel linear Rate-Quantization model is used for selecting quantization parameters for macroblocks. Implementation and experimental results show that the proposed algorithm can provide accurate bit allocation, and can effectively alleviate visual quality degradation after scene changes. This rate adaptation scheme can be used to provide flexible video bit rate adaptation for transmission of pre-encoded video over heterogeneous networks.

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“…It includes rapid motion of moving objects as well as changes to different visual content. Saw [11] has classified scene variations into different categories, including general scenes, rapid motion, panning, zooming, and scene cuts. Different metrics have been introduced in the literature to describe the non-stationary nature and scene variation of digital video scene.…”

Section: Frame -Layer Bit Allocation and Scene Contextmentioning

confidence: 99%

“…Similar as in [9], in this work we use the average variance of a frame as the energy measure of a frame, which is defined as Besides visual content, scene change is another critical element that affects video quality. In [11], the variance between two pictures is used to detect the scene change. It is defined as: where the indices i and j represent the pixels in the row and column directions respectively, µ dif is mean value of the difference picture, dif(k), which is obtained by subtracting the previous picture from the current picture on a pixel-by-pixel basis.…”

Section: Frame -Layer Bit Allocation and Scene Contextmentioning

confidence: 99%

A frame-layer bit allocation for H.263+

Ribas-Corbera

Lei²

2000

IEEE Trans. Circuits Syst. Video Technol.

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In order to transmit pre-encoded digital video over heterogeneous networks, it is necessary to employ transcoding techniques that convert pre-encoded video streams into different bit rate and quality. The specified problem is referred to as rate shaping or rate adaptation. In this work, we propose a new frame layer bit allocation for H.263+ based video transcoding. In this scheme, scene context statistics from the incoming video stream are utilized to detect scene changes and determine output frame t ypes. Bit budget is allocated to frames according to their energy and frame types. Implementation and experimental results show that the proposed algorithm can improve the video quality and effectively alleviate visual quality degradation after scene changes. This rate adaptation scheme can be used to provide flexible video bit rate adaptation for transmission compressed digital video over heterogeneous networks.

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Rate-Quality Optimized Video Coding

Cited by 13 publications

References 0 publications

Medical Quality of Service for Wireless Ultrasound Streaming in Robotic Tele-Ultrasonography System

Medical Quality of Service for Wireless Ultrasound Streaming in Robotic Tele-Ultrasonography System

Rate adaptation transcoding for precoded video streams

A frame-layer bit allocation for H.263+

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