基于码流的网络视频无参考质量评估研究
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摘要
网络视频质量评估是保证网络视频业务质量的关键技术本文深入研究了网络视频质量评估方法，提出了几种基于码流的网络视频质量评估方法：一种包层评估模型和两种比特流层评估模型，以及一种服务于包层评估模型的帧类型检测方法
主要研究成果为：
1.为了提高视频质量包层评估模型的性能，提出了一种帧类型检测方法考虑到不同类型视频帧压缩数据量的特点，利用动态阈值法初步估计各视频帧的类型；利用图像组(GOP)的周期性对初步估计结果进行修正；利用Spearman秩相关系数的概念判定B帧的预测结构
2.为了实现对网络视频的质量进行实时监控，提出了一种考虑视频内容运动特性的网络视频编码失真的包层评估模型通过帧类型检测方法确定各视频帧类型后，结合I帧编码比特数与P帧编码比特数的特点，提出一种反映视频内容运动特性的时间复杂度，并将其集成到编码比特率模型中，从而能对具有不同内容的网络视频的编码失真进行有效地评估
3.为了获取更准确的网络视频质量，通过对网络视频流数据包头信息和载荷信息的解析，提出一种内容自适应的网络视频编码失真的比特流层评估模型首先建立人眼感知的编码失真与量化参数的基本关系模型；考虑到人眼感知的编码失真还依赖于视频内容的空域特性和时域特性，使用量化参数和残差像素分布的尺度参数预测空间复杂度，使用带加权的运动矢量预测时间复杂度，进而结合基本关系模型建立起内容自适应的视频编码失真的比特流层评估模型
4.为了能获取更为精确的视频帧质量并为数据包丢失评估提供视频帧的基准质量，提出了一种基于帧质量的H.264/AVC网络视频编码失真评估首先通过主观评估实验分析确定量化参数与视频帧编码失真的基本关系模型，然后利用量化参数和I帧编码比特率预测I帧的空间复杂度，利用运动矢量信息预测P帧的时间复杂度，最后结合人类视觉系统的空域掩盖效应和时域掩盖效应得到各视频帧的编码失真，进而联合各帧质量得到视频质量
Quality assessment of networked videos is the key to ensure the quality of servicefor networked video applications. In this dissertation, the quality assessment fornetworked video is explored, and several quality assessment methods using thebitstream for networked video are proposed, including a packet-layer assessment model,two bitstream-layer assessment models and a frame type detection method forpacket-layer assessment models.
The major contributions of this dissertation are summarized as follows:
1. To improve the performance of packet-layer quality assessment of networkedvideos，a frame type detection method is proposed. Considering the characteristics ofthe compressed data of each type frame, dynamic thresholds are employed to roughlyestimate the type of each frame. Then, periodicity of the group of pictures (GOP) isused to polish the preliminary estimation. Finally, prediction structure of B-frames isdeterm ned us ng Spearman’s Rank orrelat on oeff c ent.
2. To realize real-time and non-intrusive quality monitoring for networked videos,a packet-layer model for coding distortion assessment is proposed by considering themotion characteristic of video content. The frame type of each video frame isdetermined using the proposed frame type detection method. Then combining thecharacteristics of the bit-rate for coding I frames and P frames, an estimation of thetemporal complexity is proposed which reflects the motion characteristic of the videocontent. The proposed temporal complexity is incorporated in the bit-rate model,making it adaptive to different video content.
3. To obtain more accurate quality of networked videos, a content-adaptivebitstream-layer (CABL) model is proposed for coding distortion assessment ofnetworked videos by analyzing the information extracted from packet headers andpayload of bitstreams. Firstly, the fundamental relationship between perceived codingdistortion and the quantization parameter (QP) is established. Then, considering the factthat the perceived coding distortion of a networked video significantly relies on both thespatial and temporal characteristics of video content, the spatial complexity is evaluatedusing the QP and the scale parameters of residual pixel distribution. Meanwhile, thetemporal complexity is obtained using the weighted motion vectors (MV). Finally, theproposed content-adaptive bitstream-layer model for coding distortion assessment isestablished by integrating these two content related factors into the fundamental relationship.
4. To obtain more accurate video frame quality and provide the benchmarkedquality of video frame for quality assessment under packet loss, a coding distortionassessment based on frame quality is proposed for H.264/AVC networked video. Firstly,the relationship of the coding distortion of the video frame and quantization parameteris modeled. Then, the quantization parameters and bit-rates of I frames are employed topredict the spatial complexity of an I frame, and the motion vectors are employed topredict the temporal complexity of P frames. And then, the perceptual coding distortionof each frame is calculated taking account of the spatial and temporal masking effect ofthe human visual system，Finally, the video quality is obtained by pooling quality ofeach frame.
主要研究成果为：
1.为了提高视频质量包层评估模型的性能，提出了一种帧类型检测方法考虑到不同类型视频帧压缩数据量的特点，利用动态阈值法初步估计各视频帧的类型；利用图像组(GOP)的周期性对初步估计结果进行修正；利用Spearman秩相关系数的概念判定B帧的预测结构
2.为了实现对网络视频的质量进行实时监控，提出了一种考虑视频内容运动特性的网络视频编码失真的包层评估模型通过帧类型检测方法确定各视频帧类型后，结合I帧编码比特数与P帧编码比特数的特点，提出一种反映视频内容运动特性的时间复杂度，并将其集成到编码比特率模型中，从而能对具有不同内容的网络视频的编码失真进行有效地评估
3.为了获取更准确的网络视频质量，通过对网络视频流数据包头信息和载荷信息的解析，提出一种内容自适应的网络视频编码失真的比特流层评估模型首先建立人眼感知的编码失真与量化参数的基本关系模型；考虑到人眼感知的编码失真还依赖于视频内容的空域特性和时域特性，使用量化参数和残差像素分布的尺度参数预测空间复杂度，使用带加权的运动矢量预测时间复杂度，进而结合基本关系模型建立起内容自适应的视频编码失真的比特流层评估模型
4.为了能获取更为精确的视频帧质量并为数据包丢失评估提供视频帧的基准质量，提出了一种基于帧质量的H.264/AVC网络视频编码失真评估首先通过主观评估实验分析确定量化参数与视频帧编码失真的基本关系模型，然后利用量化参数和I帧编码比特率预测I帧的空间复杂度，利用运动矢量信息预测P帧的时间复杂度，最后结合人类视觉系统的空域掩盖效应和时域掩盖效应得到各视频帧的编码失真，进而联合各帧质量得到视频质量
Quality assessment of networked videos is the key to ensure the quality of servicefor networked video applications. In this dissertation, the quality assessment fornetworked video is explored, and several quality assessment methods using thebitstream for networked video are proposed, including a packet-layer assessment model,two bitstream-layer assessment models and a frame type detection method forpacket-layer assessment models.
The major contributions of this dissertation are summarized as follows:
1. To improve the performance of packet-layer quality assessment of networkedvideos，a frame type detection method is proposed. Considering the characteristics ofthe compressed data of each type frame, dynamic thresholds are employed to roughlyestimate the type of each frame. Then, periodicity of the group of pictures (GOP) isused to polish the preliminary estimation. Finally, prediction structure of B-frames isdeterm ned us ng Spearman’s Rank orrelat on oeff c ent.
2. To realize real-time and non-intrusive quality monitoring for networked videos,a packet-layer model for coding distortion assessment is proposed by considering themotion characteristic of video content. The frame type of each video frame isdetermined using the proposed frame type detection method. Then combining thecharacteristics of the bit-rate for coding I frames and P frames, an estimation of thetemporal complexity is proposed which reflects the motion characteristic of the videocontent. The proposed temporal complexity is incorporated in the bit-rate model,making it adaptive to different video content.
3. To obtain more accurate quality of networked videos, a content-adaptivebitstream-layer (CABL) model is proposed for coding distortion assessment ofnetworked videos by analyzing the information extracted from packet headers andpayload of bitstreams. Firstly, the fundamental relationship between perceived codingdistortion and the quantization parameter (QP) is established. Then, considering the factthat the perceived coding distortion of a networked video significantly relies on both thespatial and temporal characteristics of video content, the spatial complexity is evaluatedusing the QP and the scale parameters of residual pixel distribution. Meanwhile, thetemporal complexity is obtained using the weighted motion vectors (MV). Finally, theproposed content-adaptive bitstream-layer model for coding distortion assessment isestablished by integrating these two content related factors into the fundamental relationship.
4. To obtain more accurate video frame quality and provide the benchmarkedquality of video frame for quality assessment under packet loss, a coding distortionassessment based on frame quality is proposed for H.264/AVC networked video. Firstly,the relationship of the coding distortion of the video frame and quantization parameteris modeled. Then, the quantization parameters and bit-rates of I frames are employed topredict the spatial complexity of an I frame, and the motion vectors are employed topredict the temporal complexity of P frames. And then, the perceptual coding distortionof each frame is calculated taking account of the spatial and temporal masking effect ofthe human visual system，Finally, the video quality is obtained by pooling quality ofeach frame.
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