No-Reference Video Quality Assessment Using the Temporal Statistics of Global and Local Image Features

Varga, Domonkos

doi:10.3390/s22249696

Cited by 4 publications

(2 citation statements)

References 84 publications

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“…The NR-VQA model proposed in [111] uses a systematic sampling of the three spatiotemporal planes, and the one proposed in [112] combines frame sampling strategy with a multi-resolution patch sampling mechanism to maintain the high-resolution quality information. The work done in [113] integrates the fusion of temporal statistics of local and global image features. Zoom-VQA [114] proposes an architecture to perceive spatiotemporal features at different levels, efficiently capturing both local and global information in regions of interest and in the whole frame.…”

Section: No-reference Quality Assessmentmentioning

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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Section: No-reference Quality Assessmentmentioning

confidence: 99%

Efficient Computational Cost Saving in Video Processing for QoE Estimation

Llorente,

Pérez,

Rodrigo

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Varga et al. [ 20 ] proposed an FLG-VQA model which extracts and integrates local and global image statistics features for quality perception. Li et al.…”

Section: Related Workmentioning

confidence: 99%

Blind Video Quality Assessment for Ultra-High-Definition Video Based on Super-Resolution and Deep Reinforcement Learning

Ying

Pan

Shi

2023

Sensors

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Ultra-high-definition (UHD) video has brought new challenges to objective video quality assessment (VQA) due to its high resolution and high frame rate. Most existing VQA methods are designed for non-UHD videos—when they are employed to deal with UHD videos, the processing speed will be slow and the global spatial features cannot be fully extracted. In addition, these VQA methods usually segment the video into multiple segments, predict the quality score of each segment, and then average the quality score of each segment to obtain the quality score of the whole video. This breaks the temporal correlation of the video sequences and is inconsistent with the characteristics of human visual perception. In this paper, we present a no-reference VQA method, aiming to effectively and efficiently predict quality scores for UHD videos. First, we construct a spatial distortion feature network based on a super-resolution model (SR-SDFNet), which can quickly extract the global spatial distortion features of UHD videos. Then, to aggregate the spatial distortion features of each UHD frame, we propose a time fusion network based on a reinforcement learning model (RL-TFNet), in which the actor network continuously combines multiple frame features extracted by SR-SDFNet and outputs an action to adjust the current quality score to approximate the subjective score, and the critic network outputs action values to optimize the quality perception of the actor network. Finally, we conduct large-scale experiments on UHD VQA databases and the results reveal that, compared to other state-of-the-art VQA methods, our method achieves competitive quality prediction performance with a shorter runtime and fewer model parameters.

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No-reference video quality assessment based on human visual perception

Zhou,

Kong,

Duan

et al. 2024

J. Electron. Imag.

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Conducting video quality assessment (VQA) for user-generated content (UGC) videos and achieving consistency with subjective quality assessment are highly challenging tasks. We propose a no-reference video quality assessment (NR-VQA) method for UGC scenarios by considering characteristics of human visual perception. To distinguish between varying levels of human attention within different regions of a single frame, we devise a dual-branch network. This network extracts spatial features containing positional information of moving objects from frame-level images. In addition, we employ the temporal pyramid pooling module to effectively integrate temporal features of different scales, enabling the extraction of inter-frame temporal information. To mitigate the time-lag effect in the human visual system, we introduce the temporal pyramid attention module. This module evaluates the significance of individual video frames and simulates the varying attention levels exhibited by humans towards frames. We conducted experiments on the KoNViD-1k, LIVE-VQC, CVD2014, and YouTube-UGC databases. The experimental results demonstrate the superior performance of our proposed method compared to recent NR-VQA techniques in terms of both objective assessment and consistency with subjective assessment.

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No-Reference Video Quality Assessment Using the Temporal Statistics of Global and Local Image Features

Cited by 4 publications

References 84 publications

Efficient Computational Cost Saving in Video Processing for QoE Estimation

Efficient Computational Cost Saving in Video Processing for QoE Estimation

Blind Video Quality Assessment for Ultra-High-Definition Video Based on Super-Resolution and Deep Reinforcement Learning

No-reference video quality assessment based on human visual perception

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