视频适配技术研究
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摘要
多媒体通讯技术的发展使得基于网络的多媒体应用日益广泛,云计算的出现更是加速了多媒体应用业务的开展,与此同时,复杂多媒体应用环境中异构的网络,多样的终端设备和复杂的应用也给多媒体技术提出了新的挑战。对于视频应用,网络的异构性要求视频码流能适配信道带宽的波动,网络丢包率的变化等；终端设备的多样性要求码流能适配设备计算、存储、显示能力的差异；而应用的复杂性则要求码流能即时满足用户个性化需求和响应新的应用请求。视频适配(Video Adaptatin)通过将输入视频信号变换成满足一定资源约束且符合用户需求的一种新的视频表达形式以适应复杂的多媒体应用环境,它是实现通用媒体访问(UMA, Universal Multimedia Access)和通用媒体体验(UMEs, Universal Multimedia Experiences)的主要途径,是视频处理与通信领域的重要研究方向。
视频适配的最终目标是实现用户“随事、随时、随地”享受高品质的多媒体服务的宏伟蓝图,需要能较好应对网络的异构性,终端设备的多样性和应用的复杂性。本文着眼于上述三个方面,提出了普适实用的视频适配方法。首先,针对网络的异构性,本文在可伸缩视频H.264/SVC本身提供的时间、空间、质量可伸缩的基础上,提出了使用冗余帧技术实现错误弹性可伸缩(Error Resilient Scalability),以满足不同丢包率的异构网络,扩展了H.264/SVC的可伸缩性。其次,针对终端设备多样性,本文在H.264/SVC提供的空间可伸缩性基础上,为满足用户的个性化需求并最大化用户体验,提出了基于感兴趣区域(Region of Intrest, ROI)1的适配技术,使得用户在网络带宽和设备显示能力受限时依然可以获得高品质个性化的视频观赏体验。进一步,考虑到现有“一步到位”的视频适配框架缺乏灵活性和智能性,难以适应云计算环境下规模化、智能化的复杂多媒体应用需求,本文基于中间媒体(Intermedia)的概念,提出了适用于云计算环境的视频适配框架,在面对云中新的(甚至未知的)大量并发的智能应用请求时依然可以即时提供高品质的多媒体服务。本文主要创新如下
1)以现有可伸缩视频编码标准为基础,提出一种基于冗余帧的视频容错适配方案,联合编码端,中间服务器以及解码端实现了视频传输错误弹性可伸缩。在编码端生成各种丢包率下的冗余帧信息,冗余帧信息代表了每帧在设定的丢包率下是否保留／去除增强层帧以及是否生成基本层冗余帧；冗余帧信息仅占很少比特,随原始码流一起传输或保存,中间服务器根据该信息以及当前丢包率进行实际的添加／删除帧的操作,保证输出码率的一致性并自适应增加码流容错性；解码端利用本文提出的基于维纳滤波和虚拟BLSkip的错误隐藏方法,充分挖掘视频层间以及帧间的相关性,最大限度的恢复解码视频质量。上述容错适配方案允许用户在差错异构网络中使用极其简单的操作提供错误弹性可伸缩,来尽可能有效的访问视频。
2)以现有可伸缩视频编码标准为基础,提出基于感兴趣区域的视频适配方案,联合编码端以及中间服务器,实现了感兴趣区域浏览的功能。在编码端提出相应的基于粒子滤波和基本层运动信息的感兴趣区域跟踪和基于率失真优化模式选择的高效感兴趣编码等关键技术,将原始视频编码形成多层码流：包括一个低分辨率、低质量的基本层,一个用户指定的高清晰度感兴趣区域,一个剩余的高清晰度背景区域。在中间服务器根据用户请求或者带宽约束,可以形成不同码率、不同质量的视频码流,并且可以优先保护感兴趣区域的清晰度,从而能满足用户个性化需求,提高用户体验。
3)基于中间媒体的概念,提出将其作为一种云媒体以实现云计算环境下的视频适配应用；设计并实现了视频适配演示系统,并通过实验验证了中间媒体的可行性。中间媒体包含了信号层和语义层描述,其中信号层描述用于适配复杂多样的终端,网络状况；语义层描述用于适配复杂甚至未知的应用需求。中间媒体通过编码端预处理生成,作为适配载体,可以存储或者传输到中间服务器,中间服务器根据具体的约束从中间媒体快速形成适配的标准码流并传输到最终用户。中间媒体的基本出发点是将复杂度从适配节点转移到编码端,因此可以支持大量并发的用户请求和智能应用。基于中间媒体的视频适配演示系统支持不同的客户端,如PC,PDA等,通过不同网络点播,并且可以支持多种信号层、语义层适配操作,如,码率、帧率、分辨率调整、视频摘要浏览等。
The development of multimedia communication technology and cloud computing has promoted multimedia applications. However, the network heterogeneity, device diversity and application complexity has also posing new chanllenges to multimedia technologies. For video applications, the network heterogeneity requires that the video bitstream should adapt to the bandwidth and packet loss rate changing; the device diversity requires that the video bitstream should adjust to the device's computation, storage and display capability; the application complexity requires that the video bitstream should meet the personalized user requirements and immediately response to new requests. Video adaptation transforms an inputted video to an outputted video in a new format or an augmented multimedia form to meet diverse resource constraints and user preferences. It is a promising technology to achieve Universal Multimedia Access (UMA) and Universal Multimedia Experiences (UME) and is an important research area.
Video adaptation aims to bring about "anything, anytime, anywhere" user experience for video applications, it should carefully adapt to the network heterogeneity, device diversity and application complexity. The main work of this paper is invesgating key technologies according to above three constraints. First, to adapt to the network heterogeneity, we propose to extend current scalabilities (temporal, spatial and quality scalability) to error resilient scalability, with redundant picture based error resilient adaptation approach. Second, to adapt to device diversity, we propose to enable personalized and high user experience, with a Region-of-Interest (ROI) enabled adaptation framework based on H.264/SVC. Thus, enjoy high video browsing experiences even when the bandwidth and the display capability is limited becomes possible. Furthermore, since traditional "one step" approaches need a tradeoff between adaptation flexibility and complexity, and lack in intelligence and are hard to achieve UME, especially under the cloud environment, where the applications become more and more mass and intelligent, we proposes a novel cloud-aware video adaptation framework based on an intermediate video format termed Intermedia, to support new and even unknown intelligent applications and provide immediate response to a large number of concurrent users. The contribution of this paper is three-fold.
1) We present a redundant picture based error resilient (ER) transmission scheme for scalable video coding (SVC) bitstream over heterogeneous networks with varying packet loss ratio (PLR). It combines different parts in video transmission, e.g. the encoder, media gateway and decoder, to achieve error resilient scalability. First, redundant picture information (RPI) is generated at the encoder under rate-distortion criterion. RPI represents whether a picture should be repeated or removed under given PLR and is transmitted to media gateway together with original SVC bitstream. Then, error resilient scalability is fulfilled at media gateway by selectively adding/removing NAL units of different video coding layers according to RPI and current network status. Finally, at the decoder, a Virtual-BLSkip and Wiener Filter based error concealment (EC) strategy is proposed to further improve the decoded video quality, which is especially suitable to conceal the loss of spatial enhancement layer. The proposed scheme scarcely affects the coding efficiency by transmitting RPI other than directly adding redundancy into original bitsteam. Meanwhile, it is able to provide error resilient scalability for SVC at media gateway with extremely low complexity.
2) We propose a H.264/SVC compliant video adaptation system which supports automatically ROI tracking and efficiently ROI coding technology to enable ROI browsing function (a desirable feature when designing a video application system). The proposed framework combines both the encoder and the media gateway. At the encoder, the base layer is coded with low quality/resolution and without ROI slice to provide basic video quality for devices with low bandwidth or small screens. The spatial enhancement layer contains ROI slices and provides higher video quality for devices with medium/high bandwidth or small/large screens. In the proposed system, ROI is first tracked with proposed particle filtering based tracking algorithm with considering base layer motion information, then, proposed rate-distortion optimized mode decision method, which improves the coding efficiency by relaxing the temporal constraints while taking into consideration the mismatch between reference frames when Background slices are discarded or kept, is used to encode ROI slice. User is allowed to choose the desired ROI when the network bandwidth or the screen size is limited, and the media gateway responses such request and extract the corresponding bitstream. In such a scenario, the proposed framework can preferentially guarantee a high quality ROI area and have the ability to maximize the user experience.
3) We propose to use Intermedia for video adaptation in cloud; we design and implement a video adaptation demo system and demonstrate the feasibility and effectiveness of Intermedia as a cloud-aware media. Intermedia is an intermediate video format that consists of both signal level and semantic level descriptions. The signal level description is used to quickly generate suitable bitstream that meets the contrants of various devices and different networks; the semantic level description is used to adapt to divers or even unknown application requirements. Intermedia is pre-generated and organized appropriately, and it can be stored in or transmitted to the media gateway. Specified application may introduce several constraints, and the target bitstream can be quickly generated from Intermedia with very low complexity. Intermedia based video adaptation framework shifts the complexity from transcoder to pre-processor, thus it has the ablity to simultaneously support a large number of concurrent users. The demo system supports various adaptation operations, such as bitrate adaptation, framerate, resolution adjustment, and video summarization, etc. and supports demands from different types of clients (e.g. PC, PDA, etc.) connected to the server through different networks.
视频适配的最终目标是实现用户“随事、随时、随地”享受高品质的多媒体服务的宏伟蓝图,需要能较好应对网络的异构性,终端设备的多样性和应用的复杂性。本文着眼于上述三个方面,提出了普适实用的视频适配方法。首先,针对网络的异构性,本文在可伸缩视频H.264/SVC本身提供的时间、空间、质量可伸缩的基础上,提出了使用冗余帧技术实现错误弹性可伸缩(Error Resilient Scalability),以满足不同丢包率的异构网络,扩展了H.264/SVC的可伸缩性。其次,针对终端设备多样性,本文在H.264/SVC提供的空间可伸缩性基础上,为满足用户的个性化需求并最大化用户体验,提出了基于感兴趣区域(Region of Intrest, ROI)1的适配技术,使得用户在网络带宽和设备显示能力受限时依然可以获得高品质个性化的视频观赏体验。进一步,考虑到现有“一步到位”的视频适配框架缺乏灵活性和智能性,难以适应云计算环境下规模化、智能化的复杂多媒体应用需求,本文基于中间媒体(Intermedia)的概念,提出了适用于云计算环境的视频适配框架,在面对云中新的(甚至未知的)大量并发的智能应用请求时依然可以即时提供高品质的多媒体服务。本文主要创新如下
1)以现有可伸缩视频编码标准为基础,提出一种基于冗余帧的视频容错适配方案,联合编码端,中间服务器以及解码端实现了视频传输错误弹性可伸缩。在编码端生成各种丢包率下的冗余帧信息,冗余帧信息代表了每帧在设定的丢包率下是否保留／去除增强层帧以及是否生成基本层冗余帧；冗余帧信息仅占很少比特,随原始码流一起传输或保存,中间服务器根据该信息以及当前丢包率进行实际的添加／删除帧的操作,保证输出码率的一致性并自适应增加码流容错性；解码端利用本文提出的基于维纳滤波和虚拟BLSkip的错误隐藏方法,充分挖掘视频层间以及帧间的相关性,最大限度的恢复解码视频质量。上述容错适配方案允许用户在差错异构网络中使用极其简单的操作提供错误弹性可伸缩,来尽可能有效的访问视频。
2)以现有可伸缩视频编码标准为基础,提出基于感兴趣区域的视频适配方案,联合编码端以及中间服务器,实现了感兴趣区域浏览的功能。在编码端提出相应的基于粒子滤波和基本层运动信息的感兴趣区域跟踪和基于率失真优化模式选择的高效感兴趣编码等关键技术,将原始视频编码形成多层码流：包括一个低分辨率、低质量的基本层,一个用户指定的高清晰度感兴趣区域,一个剩余的高清晰度背景区域。在中间服务器根据用户请求或者带宽约束,可以形成不同码率、不同质量的视频码流,并且可以优先保护感兴趣区域的清晰度,从而能满足用户个性化需求,提高用户体验。
3)基于中间媒体的概念,提出将其作为一种云媒体以实现云计算环境下的视频适配应用；设计并实现了视频适配演示系统,并通过实验验证了中间媒体的可行性。中间媒体包含了信号层和语义层描述,其中信号层描述用于适配复杂多样的终端,网络状况；语义层描述用于适配复杂甚至未知的应用需求。中间媒体通过编码端预处理生成,作为适配载体,可以存储或者传输到中间服务器,中间服务器根据具体的约束从中间媒体快速形成适配的标准码流并传输到最终用户。中间媒体的基本出发点是将复杂度从适配节点转移到编码端,因此可以支持大量并发的用户请求和智能应用。基于中间媒体的视频适配演示系统支持不同的客户端,如PC,PDA等,通过不同网络点播,并且可以支持多种信号层、语义层适配操作,如,码率、帧率、分辨率调整、视频摘要浏览等。
The development of multimedia communication technology and cloud computing has promoted multimedia applications. However, the network heterogeneity, device diversity and application complexity has also posing new chanllenges to multimedia technologies. For video applications, the network heterogeneity requires that the video bitstream should adapt to the bandwidth and packet loss rate changing; the device diversity requires that the video bitstream should adjust to the device's computation, storage and display capability; the application complexity requires that the video bitstream should meet the personalized user requirements and immediately response to new requests. Video adaptation transforms an inputted video to an outputted video in a new format or an augmented multimedia form to meet diverse resource constraints and user preferences. It is a promising technology to achieve Universal Multimedia Access (UMA) and Universal Multimedia Experiences (UME) and is an important research area.
Video adaptation aims to bring about "anything, anytime, anywhere" user experience for video applications, it should carefully adapt to the network heterogeneity, device diversity and application complexity. The main work of this paper is invesgating key technologies according to above three constraints. First, to adapt to the network heterogeneity, we propose to extend current scalabilities (temporal, spatial and quality scalability) to error resilient scalability, with redundant picture based error resilient adaptation approach. Second, to adapt to device diversity, we propose to enable personalized and high user experience, with a Region-of-Interest (ROI) enabled adaptation framework based on H.264/SVC. Thus, enjoy high video browsing experiences even when the bandwidth and the display capability is limited becomes possible. Furthermore, since traditional "one step" approaches need a tradeoff between adaptation flexibility and complexity, and lack in intelligence and are hard to achieve UME, especially under the cloud environment, where the applications become more and more mass and intelligent, we proposes a novel cloud-aware video adaptation framework based on an intermediate video format termed Intermedia, to support new and even unknown intelligent applications and provide immediate response to a large number of concurrent users. The contribution of this paper is three-fold.
1) We present a redundant picture based error resilient (ER) transmission scheme for scalable video coding (SVC) bitstream over heterogeneous networks with varying packet loss ratio (PLR). It combines different parts in video transmission, e.g. the encoder, media gateway and decoder, to achieve error resilient scalability. First, redundant picture information (RPI) is generated at the encoder under rate-distortion criterion. RPI represents whether a picture should be repeated or removed under given PLR and is transmitted to media gateway together with original SVC bitstream. Then, error resilient scalability is fulfilled at media gateway by selectively adding/removing NAL units of different video coding layers according to RPI and current network status. Finally, at the decoder, a Virtual-BLSkip and Wiener Filter based error concealment (EC) strategy is proposed to further improve the decoded video quality, which is especially suitable to conceal the loss of spatial enhancement layer. The proposed scheme scarcely affects the coding efficiency by transmitting RPI other than directly adding redundancy into original bitsteam. Meanwhile, it is able to provide error resilient scalability for SVC at media gateway with extremely low complexity.
2) We propose a H.264/SVC compliant video adaptation system which supports automatically ROI tracking and efficiently ROI coding technology to enable ROI browsing function (a desirable feature when designing a video application system). The proposed framework combines both the encoder and the media gateway. At the encoder, the base layer is coded with low quality/resolution and without ROI slice to provide basic video quality for devices with low bandwidth or small screens. The spatial enhancement layer contains ROI slices and provides higher video quality for devices with medium/high bandwidth or small/large screens. In the proposed system, ROI is first tracked with proposed particle filtering based tracking algorithm with considering base layer motion information, then, proposed rate-distortion optimized mode decision method, which improves the coding efficiency by relaxing the temporal constraints while taking into consideration the mismatch between reference frames when Background slices are discarded or kept, is used to encode ROI slice. User is allowed to choose the desired ROI when the network bandwidth or the screen size is limited, and the media gateway responses such request and extract the corresponding bitstream. In such a scenario, the proposed framework can preferentially guarantee a high quality ROI area and have the ability to maximize the user experience.
3) We propose to use Intermedia for video adaptation in cloud; we design and implement a video adaptation demo system and demonstrate the feasibility and effectiveness of Intermedia as a cloud-aware media. Intermedia is an intermediate video format that consists of both signal level and semantic level descriptions. The signal level description is used to quickly generate suitable bitstream that meets the contrants of various devices and different networks; the semantic level description is used to adapt to divers or even unknown application requirements. Intermedia is pre-generated and organized appropriately, and it can be stored in or transmitted to the media gateway. Specified application may introduce several constraints, and the target bitstream can be quickly generated from Intermedia with very low complexity. Intermedia based video adaptation framework shifts the complexity from transcoder to pre-processor, thus it has the ablity to simultaneously support a large number of concurrent users. The demo system supports various adaptation operations, such as bitrate adaptation, framerate, resolution adjustment, and video summarization, etc. and supports demands from different types of clients (e.g. PC, PDA, etc.) connected to the server through different networks.
引文
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