Yazar "Bayrak, Huseyin" seçeneğine göre listele

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    An Abstraction and Structural Information Based Depth Perception Evaluation Metric
    (Ieee, 2017) Nur Yilmaz, Gokce; Bayrak, Huseyin
    Developing reliable and efficient 3 Dimensional (3D) video depth perception evaluation metrics is currently a trending research topic for supporting the advancement of the 3D video services. This support can be proliferated by utilizing effective 3D video features while modeling these metrics. In this study, a Reduced Reference (RR) depth perception evaluation metric using significant depth level and structural information as effective 3D video features is developed. The significant depth level and structural information in the Depth Maps (DM) are determined using abstraction filter and Canny edge detection algorithm, respectively. The performance assessment results of the proposed RR metric present that it is quite effective for ensuring advanced 3D video services.
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    A depth perception evaluation metric for immersive user experience towards 3D multimedia services
    (Springer, 2019) Bayrak, Huseyin; Yilmaz, Gokce Nur
    The interest of users towards three-dimensional (3D) video is gaining momentum due to the recent breakthroughs in 3D video entertainment, education, network, etc. technologies. In order to speed up the advancement of these technologies, monitoring quality of experience of the 3D video, which focuses on end user's point of view rather than service-oriented provisions, becomes a central concept among the researchers. Thanks to the stereoscopic viewing ability of human visual system (HVS), the depth perception evaluation of the 3D video can be considered as one of the most critical parts of this central concept. Due to the lack of efficiently and widely utilized objective metrics in literature, the depth perception assessment can currently only be ensured by cost and time-wise troublesome subjective measurements. Therefore, a no-reference objective metric, which is highly effective especially for on the fly depth perception assessment, is developed in this paper. Three proposed algorithms (i.e., Z direction motion, structural average depth and depth deviation) significant for the HVS to perceive the depth of the 3D video are integrated together while developing the proposed metric. Considering the outcomes of the proposed metric, it can be clearly stated that the provision of better 3D video experience to the end users can be accelerated in a timely fashion for the Future Internet multimedia services.
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    No-Reference Evaluation of 3 Dimensional Video Quality Using Spatial and Frequency Domain Components
    (Ieee, 2018) Bayrak, Huseyin; Nur Yilmaz, Gokce
    Video Quality Assessment (VQA) plays an important role both for evaluating the performance of the transmitter-receiver system and for delivering the video in an efficient manner via the feedback it provides to the transmitter side. Full Reference (FR) VQA metrics currently utilized in the literature are not too efficient during the applications due to the requirement of the original video sequence at the receiver side. Therefore, the tendency of the researchers is recently on to develop Reduced Reference (RR) or No-Reference (NR) VKD metrics. In this paper, a NR VKD metric considering spatial and frequency domain components of the color and depth map based 3 Dimensional (3D) video important for Human Visual System (HVS) is developed. Canny operator which is an efficient algorithm to extract edge information is used to obtain the components in the spatial domain. Discrete Cosine Transform (DCT) is exploited to obtain the components in the frequency domain. The efficient results obtained show that the proposed algorithm is capable of superseding the FR metrics existing in the literature.
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    Scene Detection via Depth Maps Of 3 Dimensional Videos
    (Ieee, 2017) Bayrak, Huseyin; Nur Yilmaz, Gokce
    Scene detection via processing of multimedia data is a significant research area for the advancement of the video technologies and applications. Currently, the scene detection is mostly performed manually. Thus, it is time consuming and costly. Therefore, it is important to develop algorithms that can automatically segment scenes to support the advancement of these technologies and applications. With the wide-spread utilization of the 3 Dimensional (3D) videos, researchers working in the field of the video scene detection start using them in this field as well. However, there is still a gap in the application of the scene detection algorithms to Depth Maps (DMs) that are a part of the 3D video and important for temporal video scene detection. In this study, dominant clusters and K-means method is proposed to detect the temporal 3D video segments using the DMs. The experimental studies performed using the scene detection method present that the video scenes can be edited efficiently without human assistance. Moreover, unlike similar studies in the literature, the proposed method can provide successful results on video sequences thanks to the dominant clusters and the K-means clustering approach utilized.
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    Structural Depth Estimation Via Depth Maps Of 3 Dimensional Videos
    (Ieee, 2016) Bayrak, Huseyin; Yilmaz, Gokce Nur
    3-Dimensional (3D) Video Quality Assessment (VQA) has been an important area for researchers working in this area. The reason is there isn't any well accepted and standardized VQA method for 3 Dimensional (3D) as for 2-Dimensional (2D) video. Depth perception assessment (DPE) is the most critical part of 3D VQA because of visual realism. Subjective tests are currently in use for the 3D VQA because there aren't any 3D VQA algorithms for measuring this perception accepted by researchers in literature. Subjective tests are not ergonomic methods from the stand point of time and cost. Therefore, it is quite important to develop objective 3D VQA metrics for predicting the depth perception of users. The VQA algorithms developed without using a reference video is called No-Reference (NR) metrics in literature and they are considered efficient compared to the other metrics. In this study, Depth Maps (DM) in 2D+depth based 3D videos are utilized to measure Structural Average Depth (SAD) in a NR manner. The results of this study presents that the YOD algorithm can be considered as a part of a 3D VQA metric assessing the depth perception and approved by researchers.
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    Z-Direction Motion Estimation In 2d+Depth Map Based 3d Video
    (Ieee, 2014) Bayrak, Huseyin; Yilmaz, Gokce Nur; Tuna, Eyup
    3-Dimensional (3D) Video Quality Assessment (VQA) has been an important area for today's researchers along with the growing interest in 3D video. Even though, 2-Dimensional (2D) objective VQA metrics that are widely utilized by researchers in literature, the case is not the same for the 3D video. Therefore, subjective tests, which are inefficient in terms of time and cost are used for the 3D VQA. The VQA metrics are divided into three categories according to the use of the reference signal in the assessment as Full-Reference (FR), Reduced-Reference (RR) or No-Reference (NR). The NR metrics are more efficient compared to the other metrics. In this study, an algorithm that estimates the backward-forward z direction motion of the depth maps in 2D+depth map based 3D videos is proposed. The results indicate that the proposed algorithm is efficient to develop a NR VQA metric in future.