Video Coding Optimization for Virtual Reality 360-Degree Source

Zhou, Yimin; Tian, Ling; Zhu, Ce; Jin, Xin; Sun, Yu

doi:10.1109/jstsp.2019.2957952

Cited by 123 publications

(57 citation statements)

References 33 publications

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“…Other studies considered the characteristics of 360-degree videos for bit allocation. [28] used 2D projection distortion as the weighted distortion of the bit allocation algorithm, and [29] developed a spherical bit-rate equalization technique for rate-distortion optimization.…”

Section: B Viewport-adaptive 360-degree Video Streamingmentioning

confidence: 99%

360Cast+: Viewport Adaptive Soft Delivery for 360-Degree Videos

Fujihashi

Saruwatari

et al. 2021

IEEE Access

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The existing viewport-adaptive 360-degree video streaming schemes encode tiled 360degree videos with digital-based compression. However, these schemes cause a cliff effect wherein the headset video quality drops when the channel signal-to-noise ratio (SNR) falls below a certain threshold. To realize high-quality wireless 360-degree video streaming, we propose a novel viewport-adaptive soft delivery scheme for 360-degree videos, called 360Cast+. 360Cast+ skips the non-linear operations in digital-based streaming and adopts power allocation and analog modulation to achieve graceful video quality improvement in unstable wireless links. In particular, 360Cast+ integrates the human vision system (HVS) and projection distortion as a perceptual weight in power allocation operations. A near-optimal low-complexity subcarrier matching algorithm was also adopted to extend 360Cast+ to fading channel environments. To reduce the effect of the prediction error, 360Cast+ uses dynamic linear regression (DLR) to predict the future orientation and future prediction error to extract the extension area around a viewport. The evaluation results demonstrated that the proposed 360Cast+ provides sophisticated video quality irrespective of the prediction error and channel conditions in orthogonal frequency-division multiplexing (OFDM)-based systems. INDEX TERMS 360-degree video delivery, soft delivery, viewport adaptive

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Section: B Viewport-adaptive 360-degree Video Streamingmentioning

confidence: 99%

360Cast+: Viewport Adaptive Soft Delivery for 360-Degree Videos

Fujihashi

Saruwatari

et al. 2021

IEEE Access

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“…There are many problems in different scenarios that need to find optimal or sub-optimal solutions for satisfying the decision-makers [54]- [56]. Complexity and non-linearity of real-world problems have encouraged researchers to develop new analytical models [57]- [61]. One of the suitable approaches to stochastically search more complex feature spaces is the application of MAs.…”

Section: Proposed Coscamentioning

confidence: 99%

Predicting Cervical Hyperextension Injury: A Covariance Guided Sine Cosine Support Vector Machine

et al. 2020

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This study proposes an effective intelligent predictive model for prediction of cervical hyperextension injury. The prediction model is constructed by combing an improved sine cosine algorithm (SCA) with support vector machines (SVM), which is named COSCA-SVM. The core of the developed model is the COSCA method that combines the opposition-based learning mechanism and covariance mechanism to boost and recover the exploratory competence of SCA. The proposed COSCA approach is utilized to optimize the two critical parameters of the SVM, and it is also employed to catch the optimal feature subset. Based on the optimal parameter combination and feature subset, COSCA-SVM is able to make self-directed prediction of cervical hyperextension injury. The proposed COSCA was compared with other well-known and effective methods using 23 benchmark problems. Simulation results verify that the proposed COSCA is significantly superior to studied methods in dealing with majority of benchmark problems. Meanwhile, the proposed COSCA-SVM is compared with six other machine learning approaches considering a reallife dataset. Results have shown that the proposed COSCA-SVM can achieve better classification routine and higher stability on all four indicators. Therefore, we can expect that COSCA-SVM can be a promising building block for predicting cervical hyperextension injury.

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“…In a large number of scenarios, many different problems required achieving optimal or sub-optimal scheme to satisfy the decision-makers [21], [22]. A fact is that complexity and non-linearity of the practical problems generate computing challenges, which has resulted in various analytical models to be designed [23]- [27]. Due to the efficiency, metaheuristic algorithms (MEAs) can serve as one available approach to stochastically probe complex search terrain.…”

Section: Proposed Sglhhomentioning

confidence: 99%

Predicting Di-2-Ethylhexyl Phthalate Toxicity: Hybrid Integrated Harris Hawks Optimization With Support Vector Machines

et al. 2020

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Phthalic acid esters (PAEs) are organic pollutants and synthetic compounds and have adverse effects on human health. In this study, we investigated whether Di-2-Ethylhexyl phthalate (DEHP), one of many PAEs, has adverse effects on rats. Adult male Sprague-Dawley rats were treated daily by oral gavage with vehicle (corn oil) or DEHP at a dose of 3000 mg/kg/day for 15 days. The results showed that DEHP caused hepatotoxicity in rats. When compared with the control group, relative liver weights, and serum alanine, aminotransferase levels significantly increased after DEHP exposure. Hepatocyte swelling and degeneration were also found in DEHP-exposed rats. This study proposes an effective intelligence framework for the prediction of DEHP poisoning. The framework is designed by integrating an enhanced Harris hawks optimization (HHO) with a support vector machine (SVM), which is called SGLHHO-SVM. The core characteristic of the developed methodology is the SGLHHO algorithm that integrates the levy mechanism and two core operators abstracted from the salp swarm algorithm and grey wolf optimizer to enhance and restore the search capabilities of the HHO. The presented SGLHHO approach is used to tackle the key parameter pair optimization of the SVM, and it is also utilized to grab the optimal feature subset. Regarding the optimal feature subset and the pair parameter simultaneously, SGLHHO-SVM can autonomously predict the DEHP poisoning. The developed SGLHHO was conducted on 23 benchmark problems and compared with other state-of-the-art and competitive methods. The results demonstrate that the designed SGLHHO performs superior to other competitors on most benchmark problems. Furthermore, the proposed SGLHHO-SVM is also compared with other machine learning algorithms on a real-life DEHP sampled data. Statistical results verify the proposal can show better predictive property and higher stability on all for metrics. Therefore, the SGLHHO-SVM may be served as a potential computer-aided tool for the prediction of DEHP poisoning. INDEX TERMS Di-2-Ethylhexyl phthalate; Hepatotoxicity; Support vector machine; Harris hawks optimization; Salp swarm algorithm; Grey wolf optimizer.

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Video Coding Optimization for Virtual Reality 360-Degree Source

Cited by 123 publications

References 33 publications

360Cast+: Viewport Adaptive Soft Delivery for 360-Degree Videos

360Cast+: Viewport Adaptive Soft Delivery for 360-Degree Videos

Predicting Cervical Hyperextension Injury: A Covariance Guided Sine Cosine Support Vector Machine

Predicting Di-2-Ethylhexyl Phthalate Toxicity: Hybrid Integrated Harris Hawks Optimization With Support Vector Machines

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