Debajyoti Pal

A No-Reference Modular Video Quality Prediction Model for H.265/HEVC and VP9 Codecs on a Mobile Device

We propose a modular no-reference video quality prediction model for videos that are encoded with H.265/HEVC and VP9 codecs and viewed on mobile devices. The impairments which can affect video transmission are classified into two broad types depending upon which layer of the TCP/IP model they originated from. Impairments from the network layer are called the network QoS factors, while those from the application layer are called the application/payload QoS factors. Initially we treat the network and application QoS factors separately and find out the 1 : 1 relationship between the respective QoS factors and the corresponding perceived video quality or QoE. The mapping from the QoS to the QoE domain is based upon a decision variable that gives an optimal performance. Next, across each group we choose multiple QoS factors and find out the QoE for such multifactor impaired videos by using an additive, multiplicative, and regressive approach. We refer to these as the integrated network and application QoE, respectively. At the end, we use a multiple regression approach to combine the network and application QoE for building the final model. We also use an Artificial Neural Network approach for building the model and compare its performance with the regressive approach.

Extending the ITU-T G.1070 Opinion Model to Support Current Generation H.265/HEVC Video Codec

Online video streaming is one of the most promising applications that is being widely used today. Such streaming videos at high definition (HD) resolution or up consume a large network bandwidth. Current generation video codecs like H.265/High Efficiency Video Coding (HEVC) and VP9 are expected to reduce this bandwidth requirement while providing an excellent viewing quality. ITU-T has developed a standardized parametric opinion model called G.1070 that tries to assess the Quality of Experience (QoE) of any multimedia content. The model outputs an overall multimedia quality Mq which is a combination of the video quality Vq and speech quality Sq. The function Vq has to be validated for different video codecs and formats by carrying out subjective experiments. In this paper we propose for the first time a set of coefficients that enables us to extend the G.1070 opinion model to support the H.265 video codec at full-HD resolution.

A Survey of Standardized Approaches towards the Quality of Experience Evaluation for Video Services: An ITU Perspective

Over the past few years there has been an exponential increase in the amount of multimedia data being streamed over the Internet. At the same time, we are also witnessing a change in the way quality of any particular service is interpreted, with more emphasis being given to the end-users. Thus, silently there has been a paradigm shift from the traditional Quality of Service approach (QoS) towards a Quality of Experience (QoE) model while evaluating the service quality. A lot of work that tries to evaluate the quality of audio, video, and multimedia services over the Internet has been done. At the same time, research is also going on trying to map the two different domains of quality metrics, i.e., the QoS and QoE domain. Apart from the work done by individual researchers, the International Telecommunications Union (ITU) has been quite active in this area of quality assessment. This is obvious from the large number of ITU standards that are available for different application types. The sheer variety of techniques being employed by ITU as well as other researchers sometimes tends to be too complex and diversified. Although there are survey papers that try to present the current state of the art methodologies for video quality evaluation, none has focused on the ITU perspective. In this work, we try to fill up this void by presenting up-to-date information on the different measurement methods that are currently being employed by ITU for a video streaming scenario. We highlight the outline of each method with sufficient detail and try to analyze the challenges being faced along with the direction of future research.

A Video Quality Prediction Model for the Elderly

In the recent times there has been a lot of effort to use the various ICT technologies available to monitor and improve the lifestyle of the older generation. In most of the countries across the globe, these elderly people have to stay alone especially during the daytime, when other family members go out for work. Thus, monitoring their presence and activities remotely through audio/video communications is a widespread practice. However, this group of elderly people generally suffers from various types of vision impairments. Hence, mere installation of surveillance audio/video systems only will not solve the problem, as they need to interact with the system and hear/watch the audio/video communications for their well-being. In this work, we carry out a subjective test on a sample population of 59 people belonging to different age groups and record their Mean Opinion Scores (MOS). Thereafter, we run the VQM algorithm over the same set of videos and observe that the scores obtained are somewhat different for the elderly people compared to the rest. Therefore, we propose a video quality prediction model based upon the Artificial Neural Networks (ANN) that gives a better prediction for our target age group. For this model, we take into consideration the network level Quality of Service (QoS) parameters only as they have a greater impact on the perceived video quality compared to other QoS factors.