In the HEVC scalable extension (SHVC), the merge mode prediction plays an important role due to its high selection probability and its low associated bitrate. In the SHVC enhancement layer (EL) merge mode prediction, the motion information is selected from the merge candidates, in this case the spatial, temporal, and inter-layer candidates. Therefore, the merge mode prediction is usually inefficient when the motion vector field (MVF) correlation between the merge candidates and the current EL block is low, especially in video sequences containing high motion activity. To address this problem, this paper proposes an improved EL merge mode prediction solution that adaptively refines the motion vector candidates by using both base layer (BL) and EL decoded information and after linearly combines the motion compensated samples with the BL reconstructed samples to achieve a better merge mode prediction quality. Experiments conducted for a rich set of test conditions have shown that significant compression efficiency gains can be achieved with the proposed improved scalable coding solution, notably up to 4.57% in BD-rate savings regarding the standard SHVC quality scalable codec.

Light Field (LF) imaging is a plenoptic data collection method enabling a wide variety of image post-processing such as 3D extraction, viewpoint change and digital refocusing. Moreover, LF provides the capability to capture rich information about a scene, e.g., texture, geometric information, etc. Therefore, a quality assessment model for LF images is needed and poses significant challenges. Many LF Image Quality Assessment (LF-IQA) metrics have been recently presented based on the unique characteristics of LF images. The state-of-the-art objective assessment metrics have taken into account the image content and human visual system such as SSIM and IW-SSIM. However, most of these metrics are designed for images and video with natural content. Additionally, other models based on the LF characteristics (e.g., depth information, angle information) trade high performance for high computational complexity, along with them possessing difficulties of implementation for LF applications due to the immense data requirements of LF images. Hence, this paper presents a novel content-adaptive LF-IQA metric to improve the conventional LF-IQA performance that is also low in computational complexity. The experimental results clearly show improved performance compared to conventional objective IQA metrics, and we also identify metrics that are well-suited for LF image assessment. In addition, we present a comprehensive content-based feature analysis to determine the most appropriate feature that influences human visual perception among the widely used conventional objective IQA metrics. Finally, a rich LF dataset is selected from the EPFL dataset, allowing for the study of light field quality by qualitative factors such as depth (wide and narrow), focus (background or foreground) and complexity (simple and complex).

The emerging scalable High Efficiency Video Coding (SHVC) video coding standard provides an efficient solution for transmission of video over heterogeneous and time dynamic networks, terminals, and usage environments. The encoding complexity and the error sensitivity associated with the efficient HEVC coding tools adopted in SHVC make this scalable codec less attractive to some emerging applications such as video surveillance, visual sensor network, and remote space transmission where these requirements are critical. To address the requirements of these application scenarios including scalability, this paper proposes a novel HEVC-based framework offering quality scalability on top of an HEVC compliant base layer while appropriately combining the predictive and distributed coding paradigms. To achieve the best enhancement layer compression efficiency, two novel coding tools are proposed, notably a machine learning-based side information creation mechanism and an adaptive correlation modeling process. The experimental results reveal that the rate-distortion performance of the proposed distributed scalable video coding-HEVC solution outperforms the relevant alternative coding solutions, notably by up to 52.9% and 23.7% BD-rate gains regarding the HEVC-Simulcast and SHVC standard solutions, respectively, for an equivalent prediction configuration, while achieving a lower encoding complexity.

Scalable high efficiency video coding (SHVC) standard is expected to play a more important role in the heterogeneous landscape of broadcasting, multimedia, networks, and various services applications as it is specified as a layered coding technique in the advanced television systems committee 3.0. However, its block-based structure of temporal and spatial prediction makes it sensitive to information loss and error propagation due to transmission errors. In this context, we propose an improved SHVC with a joint layer prediction (JLP) solution which adaptively combines the decoded information from the base and the enhancement layers to create an additional reference for the SHVC enhancement encoder. To optimize the quality of the joint prediction, the minimum mean square error estimation is executed in computing a combination factor which gives weights to each contribution of the decoded information from the layers. In addition, the proposed JLP is integrated into the SHVC decoder to work as an error concealment solution to mitigate the error propagation happening inevitably in practical video transmission. Experiments have shown that the proposed SHVC framework significantly outperforms its relevant benchmarks, notably by up to 14.8% in bitrate reduction with respect to the standard SHVC codec. The proposed SHVC error concealment strategy also greatly improves the concealed picture quality as well as reducing the problem of error propagation when compared to conventional error concealment approaches.

The recent development of advanced television systems has demonstrated a need for an efficient video conversion technique. In this scenario, frame rate up conversion (FRUC) solutions play an important role due to their benefits in both increasing the viewing quality experience and reducing the cost of video transmission. However, with the recent increase in video resolution, notably from standard definition to high definition (HD) and ultra HD, FRUC now requires not only better interpolated frame quality but also lower FRUC time processing. Considering this problem, this study proposes a novel statistical learning based adaptive search range (SR) solution to enable an effective FRUC mechanism. In the proposed adaptive SR solution, a set of spatial‐temporal features are carefully defined and exploited to adaptively assign an appropriate SR value to each considered block, notably by formulating the SR adaptation as a classification problem and using the well‐known support vector machine framework for the classification task. Experimental results conducted on a rich set of common video test sequences show the advantages of the proposed adaptive SR solution, notably in both interpolated frame quality improvement and time processing reduction.