ChipQA: No-Reference Video Quality Prediction via Space-Time Chips

Ebenezer, Joshua P.; Shang, Zaixi; Wu, Yongjun; Wei, Hai; Sethuraman, Sriram; Bovik, Alan C.

doi:10.1109/tip.2021.3112055

Cited by 38 publications

(14 citation statements)

References 54 publications

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“…Evaluation protocols. We randomly subdivided the database into training and test sets with 80% and 20% of the data following the convention [12,15,16,27,37]. We repeated the train-test process over 100 random repeats.…”

Section: Methodsmentioning

confidence: 99%

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Blind Video Quality Assessment via Space-Time Slice Statistics

Hao

et al. 2022

2022 IEEE International Conference on Image Processing (ICIP)

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User-generated contents (UGC) have gained increased attention in the video quality community recently. Perceptual video quality assessment (VQA) of UGC videos is of great significance for content providers to monitor, process, and deliver massive numbers of UGC videos. Blind video quality prediction of UGC videos is challenging since complex mixtures of spatial and temporal distortions contribute to the overall perceptual quality. In this paper, we develop a simple, effective, and efficient blind VQA framework (STS-QA) based on the statistical analysis of space-time slices (STS) of videos. Specifically, we extract spatio-temporal statistical features along different orientations of video STS, that capture directional global motion, then train a shallow quality predictor. The proposed framework can be used to easily extend any existing video/image quality model to account for temporal or motion regularities. Our experimental results on three publicly available UGC databases demonstrate that our proposed STS-QA model can significantly boost prediction performance compared to baselines. The code will be released at: https://github.com/uniqzheng/STS_BVQA.

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Section: Methodsmentioning

confidence: 99%

“…A recent, highly relevant model called ChipQA [27] explored the concepts of space-time "chips" by obtaining local, oriented cuts of a video volume. They proposed to implicitly model space-time video naturalness using ST chips.…”

Section: Related Workmentioning

confidence: 99%

Blind Video Quality Assessment via Space-Time Slice Statistics

Hao

et al. 2022

2022 IEEE International Conference on Image Processing (ICIP)

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“…[ 16 ] proposed an asymmetric generalized Gaussian distribution (AGGD) to simulate the MSCN coefficients of distorted video and the statistics of the bandpass filtered output, which are used to predict the quality score by SVR. ChipQA [ 17 ] tracked and cropped video regions where motion information existed to obtain localized spatiotemporal slices, and outputted natural video statistical parameters for those extracted slices to perceive video quality. Chen 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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“…We evaluate a completely blind No-Reference (NR) algorithm, NIQE, and also train and evaluate ChipQA [19], a leading NR VQA algorithm. The results are presented in Table V.…”

Section: No-reference Algorithmsmentioning

confidence: 99%

Subjective and Objective Quality Assessment of High-Motion Sports Videos at Low-Bitrates

Ebenezer

Chen

et al. 2022

2022 IEEE International Conference on Image Processing (ICIP)

Self Cite

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Videos often have to be transmitted and stored at low bitrates due to poor network connectivity during adaptive bitrate streaming. Designing optimal bitrate ladders that would select the perceptually-optimized resolution, frame-rate, and compression level for low-bitrate videos for adaptive streaming across the internet is therefore a task of great interest. Towards that end, we conducted the first large-scale study of medium and low-bitrate videos from live sports for two codecs (Elemental AVC and HEVC) and created the Amazon Prime Video Low-Bitrate Sports (APV LBS) dataset. The study involved 94 participants and 742 videos, with more than 23,000 human opinion scores collected in total. We analyzed the data obtained and we also conducted an extensive evaluation of objective Video Quality Assessment (VQA) algorithms and benchmarked their performance, and make recommendations on bitrate ladder design. We're making the metadata and VQA features available at https://github.com/JoshuaEbenezer/lbmfr-public.

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ChipQA: No-Reference Video Quality Prediction via Space-Time Chips

Cited by 38 publications

References 54 publications

Blind Video Quality Assessment via Space-Time Slice Statistics

Blind Video Quality Assessment via Space-Time Slice Statistics

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

Subjective and Objective Quality Assessment of High-Motion Sports Videos at Low-Bitrates

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