Comparison of Objective Video Quality Assessment Methods on Videos With Different Spatial Resolutions

Vlaovic, Jelena; Vranješ, Mario; Grabic, Dominik; Samardžija, Dragan

doi:10.1109/iwssip.2019.8787324

Cited by 11 publications

(8 citation statements)

References 14 publications

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“…In addition to objective quality testing, subjective testing was performed for all 190 video sequences. The subjective VQA measurement was done in a controlled environment with 26 inexperienced viewers [37]. After conducting the experiment, MOS was calculated as a mean value of gathered results for each video sequence.…”

Section: Resultsmentioning

confidence: 99%

Evaluation of objective video quality assessment methods on video sequences with different spatial and temporal activity encoded at different spatial resolutions

Vlaovic

Žagar

Rimac-Drlje

et al. 2021

Int. j. electr. comput. eng. syst. (Online)

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With the development of Video on Demand applications due to the availability of high-speed internet access, adaptive streaming algorithms have been developing and improving. The focus is on improving user’s Quality of Experience (QoE) and taking it into account as one of the parameters for the adaptation algorithm. Users often experience changing network conditions, so the goal is to ensure stable video playback with satisfying QoE level. Although subjective Video Quality Assessment (VQA) methods provide more accurate results regarding user’s QoE, objective VQA methods cost less and are less time-consuming. In this article, nine different objective VQA methods are compared on a large set of video sequences with various spatial and temporal activities. VQA methods used in this analysis are: Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), MultiScale Structural Similarity Index (MS-SSIM), Video Quality Metric (VQM), Mean Sum of Differences (DELTA), Mean Sum of Absolute Differences (MSAD), Mean Squared Error (MSE), Netflix Video Multimethod Assessment Fusion (Netflix VMAF) and Visual Signal-to-Noise Ratio (VSNR). The video sequences used for testing purposes were encoded according to H.264/AVC with twelve different target coding bitrates, at three different spatial resolutions (resulting in a total of 190 sequences). In addition to objective quality assessment, subjective quality assessment was performed for these sequences. All results acquired by objective VQA methods have been compared with subjective Mean Opinion Score (MOS) results using Pearson Linear Correlation Coefficient (PLCC). Measurement results obtained on a large set of video sequences with different spatial resolutions show that VQA methods like SSIM and VQM correlate better with MOS results compared to PSNR, SSIM, VSNR, DELTA, MSE, VMAF and MSAD. However, the PLCC results for SSIM and VQM are too low (0.7799 and 0.7734, respectively), for the usage of these methods in streaming services instead of subjective testing. These results suggest that more efficient VQA methods should be developed to be used in streaming testing procedures as well as to support the video segmentation process. Furthermore, when comparing results obtained for different spatial resolutions, it can be concluded that the quality of video sequences encoded at lower spatial resolutions in cases of lower target coding bitrate is higher compared to the quality of video sequences encoded at higher spatial resolutions at the same target coding bitrate, particularly when video sequences with higher spatial and temporal information are used.

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Section: Resultsmentioning

confidence: 99%

Evaluation of objective video quality assessment methods on video sequences with different spatial and temporal activity encoded at different spatial resolutions

Vlaovic

Žagar

Rimac-Drlje

et al. 2021

Int. j. electr. comput. eng. syst. (Online)

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show abstract

“…The quality is measured by the SSIM FR IQA [41]. This metric has been widely used because of its simplicity and good results obtained in comparative studies between different metrics [122], [123]. SSIM is based on measuring the similarities of luminance, contrast, and structure between the reference and the distorted image.…”

Section: A Spatial Redundancymentioning

confidence: 99%

Efficient Computational Cost Saving in Video Processing for QoE Estimation

Llorente,

Pérez,

Rodrigo

et al. 2024

IEEE Access

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No-Reference video quality assessment has become a trending and challenging hot topic in estimating perceived quality in audiovisual content. In this paper, we present a proposal to considerably reduce the computational cost of video processing without losing accuracy in QoE estimation. Tests have been performed using the Video-MOS SaaS solution, a hybrid NR-VQA solution based on perceptible video distortions and a machine learning approach. After exploring the spatial and temporal redundancy present in a video sequence, the final approach combines video metric feature extraction in both high and low video resolution, together with a specific frame selection based on a uniform temporal sampling and frame type at the video coding level. An extensive validation with more than 144 hours of audiovisual content from six of the most important HD channels of DTT in Spain demonstrates the validity of the approach, ensuring real-time application on the test device, with computational cost savings of 94.96% and an obtained MOS error of 0.1144, in more than 174000 3-second measurements.INDEX TERMS Computational cost, feature extraction, I frames, machine learning, Mean Opinion Score (MOS), no-reference, video quality assessment, perceived quality, Quality of Experience (QoE), video processing.

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“…This is the reason why objective methods are more attractive to researchers and a lot of work has been done towards developing objective quality metrics [21]. Objective methods can be classified into full reference, reduced reference and no-reference methods [22]. Full-reference methods require the entire video for comparison with the distorted one.…”

Section: Introductionmentioning

confidence: 99%

“…In reduced-reference methods, the comparison occurs between the distorted video and part of the original one. In no-reference methods, the original video is not available when assessing the distorted video [22].…”

Section: Introductionmentioning

confidence: 99%

Integrating Visual and Network Data with Deep Learning for Streaming Video Quality Assessment

Margetis

Tsagkatakis

Stamou

et al. 2023

Sensors

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Existing video Quality-of-Experience (QoE) metrics rely on the decoded video for the estimation. In this work, we explore how the overall viewer experience, quantified via the QoE score, can be automatically derived using only information available before and during the transmission of videos, on the server side. To validate the merits of the proposed scheme, we consider a dataset of videos encoded and streamed under different conditions and train a novel deep learning architecture for estimating the QoE of the decoded video. The major novelty of our work is the exploitation and demonstration of cutting-edge deep learning techniques in automatically estimating video QoE scores. Our work significantly extends the existing approach for estimating the QoE in video streaming services by combining visual information and network conditions.

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Comparison of Objective Video Quality Assessment Methods on Videos With Different Spatial Resolutions

Cited by 11 publications

References 14 publications

Evaluation of objective video quality assessment methods on video sequences with different spatial and temporal activity encoded at different spatial resolutions

Evaluation of objective video quality assessment methods on video sequences with different spatial and temporal activity encoded at different spatial resolutions

Efficient Computational Cost Saving in Video Processing for QoE Estimation

Integrating Visual and Network Data with Deep Learning for Streaming Video Quality Assessment

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