| 1. |
- Ahmad, Waqas, et al.
(author)
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Computationally Efficient Light Field Image Compression Using a Multiview HEVC Framework
- 2019
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In: IEEE Access. - 2169-3536. ; 7, s. 143002-143014
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Journal article (peer-reviewed)abstract
- The acquisition of the spatial and angular information of a scene using light eld (LF) technologies supplement a wide range of post-processing applications, such as scene reconstruction, refocusing, virtual view synthesis, and so forth. The additional angular information possessed by LF data increases the size of the overall data captured while offering the same spatial resolution. The main contributor to the size of captured data (i.e., angular information) contains a high correlation that is exploited by state-of-the-art video encoders by treating the LF as a pseudo video sequence (PVS). The interpretation of LF as a single PVS restricts the encoding scheme to only utilize a single-dimensional angular correlation present in the LF data. In this paper, we present an LF compression framework that efciently exploits the spatial and angular correlation using a multiview extension of high-efciency video coding (MV-HEVC). The input LF views are converted into multiple PVSs and are organized hierarchically. The rate-allocation scheme takes into account the assigned organization of frames and distributes quality/bits among them accordingly. Subsequently, the reference picture selection scheme prioritizes the reference frames based on the assigned quality. The proposed compression scheme is evaluated by following the common test conditions set by JPEG Pleno. The proposed scheme performs 0.75 dB better compared to state-of-the-art compression schemes and 2.5 dB better compared to the x265-based JPEG Pleno anchor scheme. Moreover, an optimized motionsearch scheme is proposed in the framework that reduces the computational complexity (in terms of the sum of absolute difference [SAD] computations) of motion estimation by up to 87% with a negligible loss in visual quality (approximately 0.05 dB).
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| 2. |
- Ghafoor, Mubeen, et al.
(author)
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Perceptually Lossless Surgical Telementoring System Based on Non-Parametric Segmentation
- 2019
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In: Journal of Medical Imaging and Health Informatics. - : American Scientific Publishers. - 2156-7018 .- 2156-7026. ; 9:3, s. 464-473
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Journal article (peer-reviewed)abstract
- Bandwidth constraint is one of the significant concerns of surgical telementoring, especially in rural areas. High-Efficiency Video Coding (H.265/HEVC) based video compression techniques have shown promising results for telementoring applications. However, there is a tradeoff between the quality of video received by the remote surgeon and the bandwidth resources required for video transmission. In order to efficiently compress and transmit real-time surgical videos, a hybrid lossless-lossy approach is proposed where surgical incision region (location of surgery) is coded in high quality while the background (non-incision) region is coded in medium to low quality depending on the nature of the region. The surgical incision region is detected based on an efficient color and location-based non-parametric segmentation approach. This approach takes explicitly into account the physiological nature of the human visual system and efficiently encodes the video by providing good overall visual impact in the location of surgery. The results of the proposed approach are shown in terms of video quality metrics such as Bjontegaard delta bitrate (BD-BR), Bjontegaard delta peak signal-to-noise ratio (BD-PSNR), and structural similarity index measurement (SSIM). Experimental results showed that in comparison with default full-frame HEVC encoding, the proposed surgical incision region based encoding achieved an average BD-BR reduction of 77.5% at high-quality settings (QP in range of 0 to 20 in surgical incision region and an increasing QP in skin and background region). The average gain in BD-PSNR of the proposed algorithm was 6.99 dB in surgical incision region at high-quality setting, and the average SSIM index came out to be 0.9926 which is only 0.006% less than the default full-frame HEVC coding. Based on these results, the proposed encoding algorithm can be considered as an efficient and effective solution for surgical telementoring systems for limited bandwidth networks.
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| 3. |
- Hassan, A., et al.
(author)
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High Efficiency Video Coding (HEVC)–Based Surgical Telementoring System Using Shallow Convolutional Neural Network
- 2019
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In: Journal of digital imaging. - : Springer Science and Business Media LLC. - 0897-1889 .- 1618-727X. ; 32:6, s. 1027-1043
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Journal article (peer-reviewed)abstract
- Surgical telementoring systems have gained lots of interest, especially in remote locations. However, bandwidth constraint has been the primary bottleneck for efficient telementoring systems. This study aims to establish an efficient surgical telementoring system, where the qualified surgeon (mentor) provides real-time guidance and technical assistance for surgical procedures to the on-spot physician (surgeon). High Efficiency Video Coding (HEVC/H.265)–based video compression has shown promising results for telementoring applications. However, there is a trade-off between the bandwidth resources required for video transmission and quality of video received by the remote surgeon. In order to efficiently compress and transmit real-time surgical videos, a hybrid lossless-lossy approach is proposed where surgical incision region is coded in high quality whereas the background region is coded in low quality based on distance from the surgical incision region. For surgical incision region extraction, state-of-the-art deep learning (DL) architectures for semantic segmentation can be used. However, the computational complexity of these architectures is high resulting in large training and inference times. For telementoring systems, encoding time is crucial; therefore, very deep architectures are not suitable for surgical incision extraction. In this study, we propose a shallow convolutional neural network (S-CNN)–based segmentation approach that consists of encoder network only for surgical region extraction. The segmentation performance of S-CNN is compared with one of the state-of-the-art image segmentation networks (SegNet), and results demonstrate the effectiveness of the proposed network. The proposed telementoring system is efficient and explicitly considers the physiological nature of the human visual system to encode the video by providing good overall visual impact in the location of surgery. The results of the proposed S-CNN-based segmentation demonstrated a pixel accuracy of 97% and a mean intersection over union accuracy of 79%. Similarly, HEVC experimental results showed that the proposed surgical region–based encoding scheme achieved an average bitrate reduction of 88.8% at high-quality settings in comparison with default full-frame HEVC encoding. The average gain in encoding performance (signal-to-noise) of the proposed algorithm is 11.5 dB in the surgical region. The bitrate saving and visual quality of the proposed optimal bit allocation scheme are compared with the mean shift segmentation–based coding scheme for fair comparison. The results show that the proposed scheme maintains high visual quality in surgical incision region along with achieving good bitrate saving. Based on comparison and results, the proposed encoding algorithm can be considered as an efficient and effective solution for surgical telementoring systems for low-bandwidth networks.
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