A Novel Video Quality Metric for Low Bit-Rate Video Considering Both Coding and Packet-Loss Artifacts

Liu, Tao; Wang, Yao; Boyce, Jill M.; Yang, Hua; Wu, Zhenyu

doi:10.1109/jstsp.2009.2015069

Cited by 38 publications

(25 citation statements)

References 14 publications

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“…Since the main goal of SUIT is delivering more frames with lower delay and lower energy consumption by decreasing the image quality to an acceptable level, it is not surprising that the average PSNR values of the images transmitted with the SUIT protocol are lower. As discussed in [44,45], 32.5 dB is a sufficient quality for our target monitoring application and can be used for object detection algorithms at the sink side. Therefore, images with 32.5 dB still have fairly good quality.…”

Section: Performance Evaluation Of Congestion Control Techniquesmentioning

confidence: 99%

Fuzzy-based congestion control for wireless multimedia sensor networks

Sonmez

İncel

Isik

et al. 2014

J Wireless Com Network

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Congestion is a challenging problem for sensor networks because it causes the waste of communication and reduces energy efficiency. Compared to traditional wireless sensor networks, the probability of congestion occurrence in wireless multimedia sensor networks is higher due to the high volume of data arising from multimedia streaming. In this article, problems for multimedia transmission over wireless multimedia sensor networks are examined and sensor fuzzy-based image transmission (SUIT); a new progressive image transport protocol is proposed as a solution. SUIT provides fuzzy logic-based congestion estimation and an efficient congestion mitigation technique which decreases the image quality on-the-fly to an acceptable level. In case of congestion, SUIT drops some packets of the frames in a smart way and thus transmits frames to the sink with lower, but acceptable quality. In this way, SUIT improves the continuity of the video streaming. We evaluate the performance of SUIT by comparing it with two different competitors. The first one is an example transport protocol, namely Fuzzy Logic-Based Congestion Estimation. The second one is a buffer occupancy-based congestion control mechanism which is commonly used in previous studies. According to the simulation results, SUIT provides better energy consumption, frame delivery, frame loss and frame latency performance than its competitors.

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Section: Performance Evaluation Of Congestion Control Techniquesmentioning

confidence: 99%

Fuzzy-based congestion control for wireless multimedia sensor networks

Sonmez

İncel

Isik

et al. 2014

J Wireless Com Network

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show abstract

“…Other recent work aims at predicting the visibility of a packet loss to the HVS [20]- [22], where the important issue of accurately evaluating the impact of packet loss on the quality of each frame has not been fully addressed. In [23], an FR metric for evaluating the quality degradation due to packet loss was presented utilizing the "PSNR drops" of erroneous frames.…”

Section: Impact Of Packet Loss On Perceivedmentioning

confidence: 99%

“…A more advanced pooling scheme reported in [4] determines a weighting factor for each frame according to its motion activity and local luminance, and then approximates the overall quality of the video sequence by averaging the weighted quality of all individual frames. In a similar spirit to quality weighting of each frame, "PSNR drop" was used in [23] for temporal pooling to account for the "forgiveness effect." With respect to frame loss, temporal quality degradation is predominately determined by the duration in which each frame is displayed (denoted as display duration).…”

Section: Temporal Poolingmentioning

confidence: 99%

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

Yang

Wan

Xie

et al. 2010

IEEE Trans. Circuits Syst. Video Technol.

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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.

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“…However, some of them propose FR 6 or RR 7 models, and/or they are not assessed using subjective measurements. 6 On the contrary, there are also some approaches that suggest the use of NR models for the same purpose of video quality estimation. In Ref.…”

Section: Introductionmentioning

confidence: 99%

A no-reference bitstream-based perceptual model for video quality estimation of videos affected by coding artifacts and packet losses

Pandremmenou

Shahid

Kondi

et al. 2015

Human Vision and Electronic Imaging XX

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In this work, we propose a No-Reference (NR) bitstream-based model for predicting the quality of H.264/AVC video sequences, affected by both compression artifacts and transmission impairments. The proposed model is based on a feature extraction procedure, where a large number of features are calculated from the packet-loss impaired bitstream. Many of the features are firstly proposed in this work, and the specific set of the features as a whole is applied for the first time for making NR video quality predictions. All feature observations are taken as input to the Least Absolute Shrinkage and Selection Operator (LASSO) regression method. LASSO indicates the most important features, and using only them, it is possible to estimate the Mean Opinion Score (MOS) with high accuracy. Indicatively, we point out that only 13 features are able to produce a Pearson Correlation Coefficient of 0.92 with the MOS. Interestingly, the performance statistics we computed in order to assess our method for predicting the Structural Similarity Index and the Video Quality Metric are equally good. Thus, the obtained experimental results verified the suitability of the features selected by LASSO as well as the ability of LASSO in making accurate predictions through sparse modeling.

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A Novel Video Quality Metric for Low Bit-Rate Video Considering Both Coding and Packet-Loss Artifacts

Cited by 38 publications

References 14 publications

Fuzzy-based congestion control for wireless multimedia sensor networks

Fuzzy-based congestion control for wireless multimedia sensor networks

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

A no-reference bitstream-based perceptual model for video quality estimation of videos affected by coding artifacts and packet losses

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