No-Reference Quality Assessment for Networked Video via Primary Analysis of Bit Stream

Abstract

Abstract-A no-reference (NR) quality measure for networked video is introduced using information extracted from the compressed bit stream without resorting to complete video decoding. This NR video quality assessment measure accounts for three key factors which affect the overall perceived picture quality of networked video, namely, picture distortion caused by quantization, quality degradation due to packet loss and error propagation, and temporal effects of the human visual system. First, the picture quality in the spatial domain is measured, for each frame, relative to quantization under an error-free transmission condition. Second, picture quality is evaluated with respect to packet loss and the subsequent error propagation. The video frame quality in the spatial domain is, therefore, jointly determined by coding distortion and packet loss. Third, a pooling scheme is devised as the last step of the proposed quality measure to capture the perceived quality degradation in the temporal domain. The results obtained by performance evaluations using MPEG-4 coded video streams have demonstrated the effectiveness of the proposed NR video quality metric.Index Terms-Coding distortion, networked video, noreference video quality assessment, packet loss, temporal pooling.

A real-time video quality estimator for emerging wireless multimedia systems

“…The prediction is based on packet header information [26,27] and prior knowledge of the media stream [28]. However, in practice, existing solutions [22][23][24][25][26][27] have not been implemented and validated in wireless multimedia systems, where the mapping of packet/network information into MOS is required.…”

“…Although attempts to assess coding quality have often focused on estimating the PSNR, the PSNR by itself, does not always correlate well with perceived quality of the HVS [25,28,29]. PSNR can only be computed once the image is received, which is not appropriate for real-time prediction systems [29][30][31].…”

“…MOS takes less time to fall when distortions appear than to rise when distortions disappear [43]. Subjective quality depends significantly on where the lost packets are located and the nature of the related video content [28]. Since perception tests are time-consuming, costly, and unable to allow the quality to be assessed during real-time service operations, instrumental assessment methods are often preferred [27].…”

Objective No-Reference Video Quality Assessment Method Based on Spatio-Temporal Pixel Analysis

“…Once this step is completed, the testing phase can be initiated, where the optimized β's are loaded and the corresponding quality score of the distorted video sequence is computed. The LM algorithm has been applied in several fields, such as applied mathematics [2], [3] and neural networks [13].…”

“…The predicted distortion is then weighted according to the temporal activities of the frame sequence. In [3] the NR quality score is obtained from the bitstream without the need for the complete video decoding. Three factors are taken into account: picture distortion caused by quantization, quality degradation due to packet loss and error propagation, and temporal effects based on the perception by the Human Visual System (HVS).…”