An evaluation framework for 360-degree video compression

Xiu, Xiaoyu; He, Ying; Ye, Yan; Vishwanath, Bharath

doi:10.1109/vcip.2017.8305084

Cited by 35 publications

(11 citation statements)

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“…Other researches on 360 • video and image have focused on the coding performance that use head-mounted displays (HMD) to perform the subjective test [5], [6], [25]. However, the HMD does not support the new projection formats.…”

Section: B Subjective Test Methodsmentioning

confidence: 99%

“…In most 360 • image services, the two-dimensional (2D) image is generated by unfolding the pixel information from the spherical space of the 360 • image to the 2D plane [6]- [8]. This operation is called projection and the main reason for this is to use already existing high efficient 2D image coding techniques because no efficient encoding techniques have been developed for the images represented in the spherical space so far.…”

Section: Introductionmentioning

confidence: 99%

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Subjective Assessment of 360° Image Projection Formats

et al. 2020

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Recently, the use of virtual reality has been spreading rapidly in many industrial fields, such as games and entertainment, and 360 • contents are widely used in various applications of the virtual reality (VR) market. The 360 • contents create a virtual space comparable to real scenes. Coupled with the rapid growth of the 360 • image/video field, many projection formats have been developed and published. However, the studies which identify the best format for a variety of applications are still limited. Therefore, it is desirable to evaluate the visual quality of the projection formats used in the current market, and the result of the evaluation can be used as an important consideration capable of providing 360 • VR service of better quality. Here, we propose a methodology for the subjective test and briefly present ten 360 • projection formats used in our experiment. The proposed methodology for the subjective test uses a combination of the Degradation Category Rating (DCR) and Pair Comparison methods. We performed the subjective test with 15 participants for each test step, the experimental setup for which followed the proposed methodology. The results show that the equirectangular projection format (best known and widely used format in most commercial products) did not give the highest quality. Hybrid equiangular cubemap and equatorial cylindrical projection gave the best quality compared with the other eight formats. Based on the results of the DCR, it is noted that different monitors (size and resolution) do not affect the ranking of 360 • image projection formats. Our work presents the ranking of ten published projection formats which is useful for identifying the best format for a future 360 • image standard. INDEX TERMS 360 • projection format, subjective evaluation, degradation category rating, pair comparison, 360 • image.

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Section: B Subjective Test Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Subjective Assessment of 360° Image Projection Formats

et al. 2020

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“…Sun et al [9] proposed the weighted-tospherically-uniform PSNR (WS-PSNR) metric, which uses all samples on the original projection plane and considers the weights according to the corresponding specific point positions on the spherical surface, but WS-PSNR cannot assess quality of omnidirectional video across different projection formats. Similarly, an area weighted spherical PSNR (AW-SPSNR) [10] is proposed, which can utilize all available samples in the projected 2D plane and does not rely on interpolation. In [16], a Voronoi-based objective quality model is proposed for omnidirectional video quality evaluation, in which the interactive look around nature and the spherical representation characteristic are taken into account.…”

Section: A Objective Vqa For Omnidirectional Videomentioning

confidence: 99%

Quality Assessment for Omnidirectional Video: A Spatio-Temporal Distortion Modeling Approach

Gao

Zhang

Smolić

2022

IEEE Trans. Multimedia

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Omnidirectional video, also known as 360-degree video, has become increasingly popular nowadays due to its ability to provide immersive and interactive visual experiences. However, the ultra high resolution and the spherical observation space brought by the large spherical viewing range make omnidirectional video distinctly different from traditional 2D video. To date, the video quality assessment (VQA) for omnidirectional video is still an open issue. The existing VQA metrics for omnidirectional video only consider the spatial characteristics of distortions, but the temporal changes of spatial distortions can also considerably influence human visual perception. In this paper, we propose a spatiotemporal modeling approach to evaluate the quality of the omnidirectional video. Firstly, we construct a spatiotemporal quality assessment unit to evaluate the average distortion in temporal dimension at the eye fixation level, based upon which the smoothed distortion value is recursively calculated and consolidated by the characteristics of temporal variations. Then, we give a detailed solution of how to to integrate the three existing spatial VQA metrics into our approach. Besides, the cross-format omnidirectional video distortion measurement is also investigated. Finally, the spatiotemporal distortion of the whole video sequence is obtained by pooling. Based on the modeling approach, a full reference objective quality assessment metric for omnidirectional video is derived, namely OV-PSNR. The experimental results show that our proposed OV-PSNR greatly improves the prediction performance of the existing VQA metrics for omnidirectional video.

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“…For objective VQA on omnidirectional video, there have been several approaches [35,38,41,45,49,50] that advance the metric of PSNR by considering the sample density of the map projection in omnidirectional video. Some applied weight allocation in the calculation of PSNR, e.g, weighted-to-spherically-uniform PSNR (WS-PSNR) [35] and area weighted spherical PSNR (AW-SPSNR) [45]; others resampled the content into a point-uniformed projection, e.g., S-PSNR [49] and PSNR in Craster parabolic projection (CPP-PSNR) [50]. In [38,41], the performance of the above objective VQA approaches was compared by measuring their correlation with the subjective quality scores.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, it is necessary to study on visual quality assessment (VQA) for omnidirectional video. Most recently, several subjective VQA approaches [20,28,30,31,39,40,47,51] and objective VQA approaches [35,38,41,45,49,50] have been proposed for either omnidirectional image or video. Among these approaches, the impact of some factors on the quality of omnidirectional video were studied, such as display types, coding schemes and sample uniformity under different map projections.…”

Section: Introductionmentioning

confidence: 99%

Bridge the Gap Between VQA and Human Behavior on Omnidirectional Video

et al. 2018

Proceedings of the 26th ACM International Conference on Multimedia

107

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Omnidirectional video enables spherical stimuli with the 360 × 180 • viewing range. Meanwhile, only the viewport region of omnidirectional video can be seen by the observer through head movement (HM), and an even smaller region within the viewport can be clearly perceived through eye movement (EM). Thus, the subjective quality of omnidirectional video may be correlated with HM and EM of human behavior. To fill in the gap between subjective quality and human behavior, this paper proposes a large-scale visual quality assessment (VQA) dataset of omnidirectional video, called VQA-OV, which collects 60 reference sequences and 540 impaired sequences. Our VQA-OV dataset provides not only the subjective quality scores of sequences but also the HM and EM data of subjects. By mining our dataset, we find that the subjective quality of omnidirectional video is indeed related to HM and EM. Hence, we develop a deep learning model, which embeds HM and EM, for objective VQA on omnidirectional video. Experimental results show that our model significantly improves the state-of-the-art performance of VQA on omnidirectional video. CCS CONCEPTS• Human-centered computing → Virtual reality; KEYWORDSOmnidirectional video, visual quality assessment, human behavior ACM Reference Format:

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An evaluation framework for 360-degree video compression

Cited by 35 publications

References 1 publication

Subjective Assessment of 360° Image Projection Formats

Subjective Assessment of 360° Image Projection Formats

Quality Assessment for Omnidirectional Video: A Spatio-Temporal Distortion Modeling Approach

Bridge the Gap Between VQA and Human Behavior on Omnidirectional Video

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