No-reference depth map quality evaluation model based on depth map edge confidence measurement in immersive video applications

When it comes to evaluating perceptual quality of digital media for overall quality of experience assessment in immersive video applications, typically two main approaches stand out: Subjective and objective quality evaluation. On one hand, subjective quality evaluation offers the best representation of perceived video quality assessed by the real viewers. On the other hand, it consumes a significant amount of time and effort, due to the involvement of real users with lengthy and laborious assessment procedures. Thus, it is essential that an objective quality evaluation model is developed. The speed-up advantage offered by an objective quality evaluation model, which can predict the quality of rendered virtual views based on the depth maps used in the rendering process, allows for faster quality assessments for immersive video applications. This is particularly important given the lack of a suitable reference or ground truth for comparing the available depth maps, especially when live content services are offered in those applications. This paper presents a no-reference depth map quality evaluation model based on a proposed depth map edge confidence measurement technique to assist with accurately estimating the quality of rendered (virtual) views in immersive multi-view video content. The model is applied for depth image-based rendering in multi-view video format, providing comparable evaluation results to those existing in the literature, and often exceeding their performance

An improved model of binocular energy calculation for full-reference stereoscopic image quality assessment

With the exponential growth of stereoscopic imaging in various applications, it has become very demanding to have a reliable quality assessment technique to measure the human perception of stereoscopic images. Quality assessment of stereoscopic visual content in the presence of artefacts caused by compression and transmission is a key component of end-to-end 3D media delivery systems. Despite a few recent attempts to develop stereoscopic image/video quality metrics, there is still a lack of a robust stereoscopic image quality metric. Towards addressing this issue, this paper proposes a full reference stereoscopic image quality metric, which mimics the human perception while viewing stereoscopic images. A signal processing model that is consistent with physiological literature is developed in the paper to simulate the behaviour of simple and complex cells of the primary visual cortex in the Human Visual System (HVS). The model is trained with two publicly available stereoscopic image databases to match the perceptual judgement of impaired stereoscopic images. The experimental results demonstrate a significant improvement in prediction performance as compared with several state-of-the-art stereoscopic image quality metrics

Towards adaptive control in smart homes: Overall system design and initial evaluation of activity recognition

This paper proposes an approach for adaptive control over devices within a smart home, by learning user behavior and preferences over time. The proposed solution leverages three components: activity recognition for realising the state of a user, ontologies for finding relevant devices within a smart home, and machine learning for decision making. In this paper, the focus is on the first component. Existing algorithms for activity recognition are systematically evaluated on a real-world dataset. A thorough analysis of the algorithms’ accuracy is presented, with focus on the structure of the selected dataset. Finally, further study of the dataset is carried out, aiming at reasoning factors that influence the activity recognition performance

Modeling user perception of 3D video based on ambient illumination context for enhanced user centric media access and consumption

For enjoying 3D video to its full extent, it is imperative that access and consumption of it is user centric, which in turn ensures improved 3D video perception. Several important factors including video characteristics, users’ preferences, contexts prevailing in various usage environments, etc have influences on 3D video perception. Thus, to assist efficient provision of user centric media, user perception of 3D video should be modeled considering the factors affecting perception. Considering ambient illumination context to model 3D video perception is an interesting research topic, which has not been particularly investigated in literature. This context is taken into account while modeling video quality and depth perception of 3D video in this paper. For the video quality perception model: motion and structural feature characteristics of color texture sequences; and for the depth perception model: luminance contrast of color texture and depth intensity of depth map sequences of 3D video are used as primary content related factors in the paper. Results derived using the video quality and depth perception models demonstrate that these models can efficiently predict user perception of 3D video considering the ambient illumination context in user centric media access and consumption environments

Stereoscopic video quality assessment using binocular energy

Stereoscopic imaging is becoming increasingly popular. However, to ensure the best quality of experience, there is a need to develop more robust and accurate objective metrics for stereoscopic content quality assessment. Existing stereoscopic image and video metrics are either extensions of conventional 2D metrics (with added depth or disparity information) or are based on relatively simple perceptual models. Consequently, they tend to lack the accuracy and robustness required for stereoscopic content quality assessment. This paper introduces full-reference stereoscopic image and video quality metrics based on a Human Visual System (HVS) model incorporating important physiological findings on binocular vision. The proposed approach is based on the following three contributions. First, it introduces a novel HVS model extending previous models to include the phenomena of binocular suppression and recurrent excitation. Second, an image quality metric based on the novel HVS model is proposed. Finally, an optimised temporal pooling strategy is introduced to extend the metric to the video domain. Both image and video quality metrics are obtained via a training procedure to establish a relationship between subjective scores and objective measures of the HVS model. The metrics are evaluated using publicly available stereoscopic image/video databases as well as a new stereoscopic video database. An extensive experimental evaluation demonstrates the robustness of the proposed quality metrics. This indicates a considerable improvement with respect to the state-of-the-art with average correlations with subjective scores of 0.86 for the proposed stereoscopic image metric and 0.89 and 0.91 for the proposed stereoscopic video metrics

Seamless video access for mobile devices by content-aware utility-based adaptation

Today's Internet multimedia services are characterized by heterogeneous networks, a wide range of terminals, diverse user preferences, and varying natural environment conditions. Heterogeneity of terminals, networks, and user preferences impose nontrivial challenges to the Internet multimedia services for providing seamless multimedia access particularly for mobile devices (e.g., laptops, tablet PCs, PDAs, mobile phones, etc.). Thus, it is essential that advanced multimedia technologies are developed to deal with these challenges. One of these technologies is video adaptation, which has gained significant importance with its main objective of enabling seamless access to video contents available over the Internet. Adaptation decision taking, which can be considered as the "brain" of video adaptation, assists video adaptation to achieve this objective. Scalable Video Coding (SVC) offers flexibility for video adaptation through providing a comprehensive set of scalability parameters (i.e., temporal, spatial, and quality) for producing scalable video streams. Deciding the best combination of scalability parameters to adapt a scalable video stream while satisfying a set of constraints (e.g., device specifics, network bandwidth, etc.) poses challenges for the existing adaptation services to enable seamless video access. To ease such challenges, an adaptation decision taking technique employing a utility-based approach to decide on the most adequate scalability parameters for adaptation operations is developed. A Utility Function (UF), which models the relationships among the scalability parameters and weights specifying the relative importance of these parameters considering video content characteristics (i.e., motion activity and structural feature), is proposed to assist the developed technique. In order to perform the developed adaptation decision taking technique, a video adaptation framework is also proposed in this paper. The adaptation experiments performed using the proposed framework prove the effectiveness of the framework to provide an important step towards enabling seamless video access for mobile devices to enhance viewing experience of users. © 2012 Springer Science+Business Media, LLC

3D video bit rate adaptation decision taking using ambient illumination context

3-Dimensional (3D) video adaptation decision taking is an open field in which not many researchers have carried out investigations yet compared to 3D video display, coding, etc. Moreover, utilizing ambient illumination as an environmental context for 3D video adaptation decision taking has particularly not been studied in literature to date. In this paper, a user perception model, which is based on determining perception characteristics of a user for a 3D video content viewed under a particular ambient illumination condition, is proposed. Using the proposed model, a 3D video bit rate adaptation decision taking technique is developed to determine the adapted bit rate for the 3D video content to maintain 3D video quality perception by considering the ambient illumination condition changes. Experimental results demonstrate that the proposed technique is capable of exploiting the changes in ambient illumination level to use network resources more efficiently without sacrificing the 3D video quality perception

A full-reference stereoscopic image quality metric based on binocular energy and regression analysis

The recent developments of 3D media technology have brought to life numerous applications of interactive entertainment such as 3D cinema, 3DTV and gaming. However, due to the data intensive nature of 3D visual content, a number of research challenges have emerged. In order to optimise the end-to-end content life-cycle, from capture to processing and delivery, Quality of Experience (QoE) has become a major driving factor. This paper presents a human-centric approach to quality estimation of 3D visual content. A full reference quality assessment method for stereoscopic images is proposed. It is based on a Human Visual System (HVS) model to estimate subjective scores of registered stereoscopic images subjected to compression losses. The model has been trained with four publicly available registered stereoscopic image databases and a fixed relationship between subjective scores and the model has been determined. The high correlation of the relationship over a large number of stimuli has proven its consistency over the state-of-the-art

3D video bit rate adaptation decision taking using ambient illumination context

3-Dimensional (3D) video adaptation decision taking is an open field in which not many researchers have carried out investigations yet compared to 3D video display, coding, etc. Moreover, utilizing ambient illumination as an environmental context for 3D video adaptation decision taking has particularly not been studied in literature to date. In this paper, a user perception model, which is based on determining perception characteristics of a user for a 3D video content viewed under a particular ambient illumination condition, is proposed. Using the proposed model, a 3D video bit rate adaptation decision taking technique is developed to determine the adapted bit rate for the 3D video content to maintain 3D video quality perception by considering the ambient illumination condition changes. Experimental results demonstrate that the proposed technique is capable of exploiting the changes in ambient illumination level to use network resources more efficiently without sacrificing the 3D video quality perception

Full-reference stereoscopic video quality assessment using a motion sensitive HVS model

Stereoscopic video quality assessment has become a major research topic in recent years. Existing stereoscopic video quality metrics are predominantly based on stereoscopic image quality metrics extended to the time domain via for example temporal pooling. These approaches do not explicitly consider the motion sensitivity of the Human Visual System (HVS). To address this limitation, this paper introduces a novel HVS model inspired by physiological findings characterising the motion sensitive response of complex cells in the primary visual cortex (V1 area). The proposed HVS model generalises previous HVS models, which characterised the behaviour of simple and complex cells but ignored motion sensitivity, by estimating optical flow to measure scene velocity at different scales and orientations. The local motion characteristics (direction and amplitude) are used to modulate the output of complex cells. The model is applied to develop a new type of full-reference stereoscopic video quality metrics which uniquely combine non-motion sensitive and motion sensitive energy terms to mimic the response of the HVS. A tailored two-stage multi-variate stepwise regression algorithm is introduced to determine the optimal contribution of each energy term. The two proposed stereoscopic video quality metrics are evaluated on three stereoscopic video datasets. Results indicate that they achieve average correlations with subjective scores of 0.9257 (PLCC), 0.9338 and 0.9120 (SRCC), 0.8622 and 0.8306 (KRCC), and outperform previous stereoscopic video quality metrics including other recent HVS-based metrics