A new video quality metric for compressed video.

Video compression enables multimedia applications such as mobile video messaging and streaming, video conferencing and more recently online social video interactions to be possible. Since most multimedia applications are meant for the human observer, measuring perceived video quality during the designing and testing of these applications is important. Performance of existing perceptual video quality measurement techniques is limited due to poor correlation with subjective quality and implementation complexity. Therefore, this thesis presents new techniques for measuring perceived quality of compressed multimedia video using computationally simple and efficient algorithms. A new full reference perceptual video quality metric called the MOSp metric for measuring subjective quality of multimedia video sequences compressed using block-based video coding algorithms is developed. The metric predicts subjective quality of compressed video using the mean squared error between original and compressed sequences, and video content. Factors which influence the visibility of compression-induced distortion such as spatial texture masking, temporal masking and cognition, are considered for quantifying video content. The MOSp metric is simple to implement and can be integrated into block-based video coding algorithms for real time quality estimations. Performance results presented for a variety of multimedia content compressed to a large range of bitrates show that the metric has high correlation with subjective quality and performs better than popular video quality metrics. As an application of the MOSp metric to perceptual video coding, a new MOSpbased mode selection algorithm for a H264/AVC video encoder is developed. Results show that, by integrating the MOSp metric into the mode selection process, it is possible to make coding decisions based on estimated visual quality rather than mathematical error measures and to achieve visual quality gain in content that is identified as visually important by the MOSp metric. The novel algorithms developed in this research work are particularly useful for integrating into block based video encoders such as the H264/AVC standard for making real time visual quality estimations and coding decisions based on estimated visual quality rather than the currently used mathematical error measures

Efficient compression of synthetic video

Streaming of on-line gaming video is a challenging problem because of the enormous amounts of video data that need to be sent during game playing, especially within the limitations of uplink capabilities. The encoding complexity is also a challenge because of the time delay while on-line gamers are communicating. The main goal of this research study is to propose an enhanced on-line game video streaming system. First, the most common video coding techniques have been evaluated. The evaluation study considers objective and subjective metrics. Three widespread video coding techniques are selected and evaluated in the study; H.264, MPEG-4 Visual and VP- 8. Diverse types of video sequences were used with different frame rates and resolutions. The effects of changing frame rate and resolution on compression efficiency and viewers‟ satisfaction are also presented. Results showed that the compression process and perceptual satisfaction are severely affected by the nature of the compressed sequence. As a result, H.264 showed higher compression efficiency for synthetic sequences and outperformed other codecs in the subjective evaluation tests. Second, a fast inter prediction technique to speed up the encoding process of H.264 has been devised. The on-line game streaming service is a real time application, thus, compression complexity significantly affects the whole process of on-line streaming. H.264 has been recommended for synthetic video coding by our results gained in codecs comparative studies. However, it still suffers from high encoding complexity; thus a low complexity coding algorithm is presented as fast inter coding model with reference management technique. The proposed algorithm was compared to a state of the art method, the results showing better achievement in time and bit rate reduction with negligible loss of fidelity. Third, recommendations on tradeoff between frame rates and resolution within given uplink capabilities are provided for H.264 video coding. The recommended tradeoffs are offered as a result of extensive experiments using Double Stimulus Impairment Scale (DSIS) subjective evaluation metric. Experiments showed that viewers‟ satisfaction is profoundly affected by varying frame rates and resolutions. In addition, increasing frame rate or frame resolution does not always guarantee improved increments of perceptual quality. As a result, tradeoffs are recommended to compromise between frame rate and resolution within a given bit rate to guarantee the highest user satisfaction. For system completeness and to facilitate the implementation of the proposed techniques, an efficient game video streaming management system is proposed. Compared to existing on-line live video service systems for games, the proposed system provides improved coding efficiency, complexity reduction and better user satisfaction

Quality-driven resource utilization methods for video streaming in wireless communication networks

This research is focused on the optimisation of resource utilisation in wireless mobile networks with the consideration of the users’ experienced quality of video streaming services. The study specifically considers the new generation of mobile communication networks, i.e. 4G-LTE, as the main research context. The background study provides an overview of the main properties of the relevant technologies investigated. These include video streaming protocols and networks, video service quality assessment methods, the infrastructure and related functionalities of LTE, and resource allocation algorithms in mobile communication systems. A mathematical model based on an objective and no-reference quality assessment metric for video streaming, namely Pause Intensity, is developed in this work for the evaluation of the continuity of streaming services. The analytical model is verified by extensive simulation and subjective testing on the joint impairment effects of the pause duration and pause frequency. Various types of the video contents and different levels of the impairments have been used in the process of validation tests. It has been shown that Pause Intensity is closely correlated with the subjective quality measurement in terms of the Mean Opinion Score and this correlation property is content independent. Based on the Pause Intensity metric, an optimised resource allocation approach is proposed for the given user requirements, communication system specifications and network performances. This approach concerns both system efficiency and fairness when establishing appropriate resource allocation algorithms, together with the consideration of the correlation between the required and allocated data rates per user. Pause Intensity plays a key role here, representing the required level of Quality of Experience (QoE) to ensure the best balance between system efficiency and fairness. The 3GPP Long Term Evolution (LTE) system is used as the main application environment where the proposed research framework is examined and the results are compared with existing scheduling methods on the achievable fairness, efficiency and correlation. Adaptive video streaming technologies are also investigated and combined with our initiatives on determining the distribution of QoE performance across the network. The resulting scheduling process is controlled through the prioritization of users by considering their perceived quality for the services received. Meanwhile, a trade-off between fairness and efficiency is maintained through an online adjustment of the scheduler’s parameters. Furthermore, Pause Intensity is applied to act as a regulator to realise the rate adaptation function during the end user’s playback of the adaptive streaming service. The adaptive rates under various channel conditions and the shape of the QoE distribution amongst the users for different scheduling policies have been demonstrated in the context of LTE. Finally, the work for interworking between mobile communication system at the macro-cell level and the different deployments of WiFi technologies throughout the macro-cell is presented. A QoEdriven approach is proposed to analyse the offloading mechanism of the user’s data (e.g. video traffic) while the new rate distribution algorithm reshapes the network capacity across the macrocell. The scheduling policy derived is used to regulate the performance of the resource allocation across the fair-efficient spectrum. The associated offloading mechanism can properly control the number of the users within the coverages of the macro-cell base station and each of the WiFi access points involved. The performance of the non-seamless and user-controlled mobile traffic offloading (through the mobile WiFi devices) has been evaluated and compared with that of the standard operator-controlled WiFi hotspots