Blind Sharpness Prediction for Ultrahigh-Definition Video Based on Human Visual Resolution

Kim, Haksub; Kim, Jongyoo; Oh, Taegeun

doi:10.1109/tcsvt.2016.2515303

Cited by 13 publications

(7 citation statements)

References 38 publications

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“…viewing geometry factors (viewing distance, display resolution, display size, and display types: flat or curved). Therefore, many existing QoE studies have applied viewing geometry to design a prediction model that reflects perceptual resolution [7,[33][34][35]. Figure 2(b) geometrically depicts an example of perceived pixel according to display type.…”

Section: A) Qoe Trend On 2d Displaymentioning

confidence: 99%

“… Foveation : The distribution of photoreceptors in the human eye is not uniform and decreases away from the center of the fovea [12,13]. This characteristic is defined as foveation and has been employed as a spatial weight of the 2D domain in many existing studies [7,12,13,31–33]. For example, when a viewer gazes at a fixation point, as shown in Fig.…”

Section: Qoe On 2d Displaymentioning

confidence: 99%

“…Therefore, when user undergoes a specific QoE, it can be seen that the QoE is likely to be induced from the concentrated area. For this reason, in many studies, the saliency prediction has been implemented through the saliency weighting on the target QoE [7,33].…”

Section: Qoe On 2d Displaymentioning

confidence: 99%

“…To overcome this, Kim et al. [33] proposed a sharpness assessment metric that takes into account various factors that affect the perceived resolution.…”

Section: Qoe On 2d Displaymentioning

confidence: 99%

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Modern trends on quality of experience assessment and future work

Kim

Ahn

Nguyen

et al. 2019

SIP

Self Cite

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Over the past 20 years, research on quality of experience (QoE) has been actively expanded even to cover aesthetic, emotional and psychological experiences. QoE has been an important research topic in determining the perceptual factors that are essential to users in keeping with the emergence of new display technologies. In this paper, we provide in-depth reviews of recent assessment studies in this field. Compared to previous reviews, our research examines the human factors observed over various recent displays and their associated assessment methods. In this study, we first provide a comprehensive QoE analysis on 2D display including image/video quality assessment (I/VQA), visual preference, and human visual system-related studies. Second, we analyze stereoscopic 3D (S3D) QoE research on the topics of I/VQA and visual discomfort from the human perception point of view on S3D display. Third, we investigate QoE in a head-mounted display-based virtual reality (VR) environment, and deal with VR sickness and 360 I/VQA with their individual approach. All of our reviews are analyzed through comparison of benchmark models. Furthermore, we layout QoE works on future display and modern deep-learning applications.

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Section: A) Qoe Trend On 2d Displaymentioning

confidence: 99%

Section: Qoe On 2d Displaymentioning

confidence: 99%

Section: Qoe On 2d Displaymentioning

confidence: 99%

“…To overcome this, Kim et al. [33] proposed a sharpness assessment metric that takes into account various factors that affect the perceived resolution.…”

Section: Qoe On 2d Displaymentioning

confidence: 99%

See 2 more Smart Citations

Modern trends on quality of experience assessment and future work

Kim

Ahn

Nguyen

et al. 2019

SIP

Self Cite

View full text Add to dashboard Cite

show abstract

“…Also, some other researchers proposed that semantic clews of multiple event recognitions should be fused by means of a deep-level learning strategy so that the issue of recognition would be solved by answering how to jointly analyse human actions, objects and scenes. That is to say, first, each type of semantic features is transmitted to an abstract path of multi-level features, with one fusion level to connect all different paths, accordingly to learn the mutually affecting relevancy of semantic clews via unsupervised transchannel coding; lastly, the question of how semantic clews compose one event and a group of events is answered by fine tuning of large-amplitude objects on the architecture [21][22][23][24]. This paper adopts a 3-layer semantic recognition approach based on key frame extraction.…”

Section: Video Semantic Analysis and Relevant Researchmentioning

confidence: 99%

Application of Video Scene Semantic Recognition Technology in Smart Video

Qin

Kang

2018

Teh. vjesn.

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Video behaviour recognition and semantic recognition understanding are important components of intelligent video analytics. Traditionally, human behaviour recognition has met problems of low recognition efficiencies and poor accuracies. For example, most existing behaviour recognition methods use the video frames obtained by even segmentation and fixed sampling as the input, which may lose important information between sampling intervals, fail to identify the key frames of the video segments and make use of the contextual semantics to understand current behaviour. In order to improve the semantic understanding capacity and efficiency of video segments, this paper adopts a 3-layer semantic recognition approach based on key frame extraction. First, it completes the segmentation for video recognition at the bottom layer, extracts the key frames in the video segments, primarily understands basic semantics of the persons' identifications, behaviours and environment, and then introduces the primarily acquired information into the middle layer for semantic integration, and through the integration of various semantics, adopts the loss function to learn the latent relationship between different modal semantics, to enhance the integrating capacity and the robustness of the character semantic integration, and finally, by overall fine tuning, semantic recognition and adjusting all the parameters of the network, completes the semantic recognition task of the video scenario. This method enjoys higher recognition accuracies based on certain datasets, capable of effectively recognizing the semantics of characters and behaviours in videos. Through practical testing, the adoption of the algorithm integrating key frame extractions with the video scene semantic recognition has improved the recognition accuracy and effect of the video character semantics.

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