Piotr Romaniak

Framework for the integrated video quality assessment

Through years of development Content Networks (CN) have become more sophisticated and more technically diverse. Modern CN are designed to be more adaptive to communication environment, devices and user requirements. However, one open issue is the still fluctuating quality of service provision. As a result user experience can be negatively affected. In order to maintain a satisfactory level of user experience it is crucial to develop a feasible solution to measure the extent to which video services meet users’ expectation. Assessing video quality with respect to users’ subjective opinions is a complex task. In this paper we address challenges of this task and design an integrated framework using a number of comprehensive functional modules. Our framework integrates objective quality assessment models of Artifacts Measurement (AM) and Quality of Delivery (QoD) approaches. Only the fittest models are activated by the framework considering requirements of individual evaluation tasks. We also introduce our recent work of realising key functional modules of the framework. Joint subjective experiments between two institutes have also been carried out for the purpose of model implementation and evaluation. Results from experiments verify the concept of an integrated framework and show the effectiveness of its key modules in estimating the quality level of video services.

Perceptual quality assessment for H.264/AVC compression

The paper proposes a No-Reference (NR) metric to objectively assess the H.264/AVC video quality. The proposed model takes into account the typical artefacts introduced by hybrid block-based motion compensated predictive video codecs as the one related to the H.264/AVC standard. More specifically, these artefacts are the blockiness introduced at the boundaries of each coded block and the temporal flickering due to different coding modes used for the same macroblock along the video sequence. Furthermore, a flickering metric for intra coded frames is also derived. The quality prediction accuracy of the proposed NR quality metric is validated over subjective data collected during a video subjective evaluation experiments. Moreover, the quality prediction accuracy is also compared with the one provided by the well known state-of-the-art Structural SIMilarity (SSIM) metric which works in a full-reference mode. The proposed metric achieves a higher Pearsons correlation coefficient with subjective scores than the one achieved by the SSIM metric.

QoE as a function of frame rate and resolution changes

Video bit rate reduction can be very important for all video streaming application. One of the possible ways to reduce bit rate is decreasing change in time or space domain i.e. changing frame rate or resolution. In this paper we present two no reference metrics mapping frame rate or resolution into MOS. Both models use simple to calculate parameters expressed by sequence spatial and temporal information. The models were estimated and verified upon distinctive video sequence sets. The considered frame rate change varies from 5 to 30 frames per second. The considered resolutions changes from SQCIF to SD.

Quality assessment for a visual and automatic license plate recognition

Video transmission and analysis is often utilized in applications outside of the entertainment sector, and generally speaking this class of video is used to perform specific tasks. Examples of these applications include security and public safety. The Quality of Experience (QoE) concept for video content used for entertainment differs significantly from the QoE of surveillance video used for recognition tasks. This is because, in the latter case, the subjective satisfaction of the user depends on achieving a given functionality. Recognizing the growing importance of video in delivering a range of public safety services, we focused on developing critical quality thresholds in license plate recognition tasks based on videos streamed in constrained networking conditions. Since the number of surveillance cameras is still growing it is obvious that automatic systems will be used to do the tasks. Therefore, the presented research includes also analysis of automatic recognition algorithms.

Quality Assessment for a Licence Plate Recognition Task Based on a Video Streamed in Limited Networking Conditions

Video transmission and analysis is often utilised in applications outside of the entertainment sector, and generally speaking this class of video is used to perform a specific task. Examples of these applications are security and public safety. The Quality of Experience (QoE) concept for video content used for entertainment differs significantly from the QoE of surveillance video used for recognition tasks. This is because, in the latter case, the subjective satisfaction of the user depends on achieving a given functionality. Moreover, such sequences have to be compressed significantly because the monitored place has to be seen on-line and it can be connected by an error prone wireless connection. Recognising the growing importance of video in delivering a range of public safety services, we focused on developing critical quality thresholds in licence plate recognition tasks based on videos streamed in constrained networking conditions.

Video Artifacts Assessment for Live Mobile Streaming Applications

Live mobile streaming applications will be among the most important applications in future wireless multimedia systems. Hence, a Quality of Experience (QoE) assessment control mechanism is an essential requirement to assure the video quality level, whiling maximizing profits to service providers and keeping and attracting new customers. This paper studies the requirements to develop a video artifacts assessment mechanism for live mobile streaming applications, introduce a new assessment solution and no-reference QoE metrics. The proposed schemes are evaluated based on psycho-physical experiments faced with artifacts measurements. The results present the benefits of the assessment mechanism in estimating the quality level of live mobile streaming applications.

Automated qualitative assessment of multi-modal distortions in digital images based on GLZ

This paper introduces a novel approach to a qualitative assessment of images affected by multi-modal distortions. The idea is to assess the image quality perceived by an end user in an automatic way in order to avoid the usual time-consuming, costly and non-repeatable method of collecting subjective scores during a psycho-physical experiment. This is achieved by computing quantitative image distortions and mapping results on qualitative scores. Useful mapping models have been proposed and constructed using the generalised linear model (GLZ), which is a generalisation of the least squares regression in statistics for ordinal data. Overall qualitative image distortion is computed based on partial quantitative distortions from component algorithms operating on specified image features. Seven such algorithms are applied to successfully analyse the seven image distortions in relation to the original image. A survey of over 12,000 subjective quality scores has been carried out in order to determine the influence of these features on the perceived image quality. The results of quantitative assessments are mapped on the surveyed scores to obtain an overall quality score of the image. The proposed models have been validated in order to prove that the above technique can be applied to automatic image quality assessment.

Assessing quality of experience for high definition video streaming under diverse packet loss patterns

Abstract This paper presents an approach for deriving a QoE model of High Definition video streaming in the presence of different patterns of packet losses. The goal was achieved by using the SSIM video quality metric, temporal pooling techniques and content characteristics. Subjective tests were performed in order to verify the proposed models. The impact of several network loss patterns on diverse video content was analysed. The paper also deals with the encountered difficulties and presents intermediate steps to give a better understanding of the final result. The research aims to evaluate the perceived performance of IPTV and video surveillance systems. The model has been evaluated in the Quality of Experience (QoE) domain. The final model is generic and shows high correlation with the subjective results.

Content driven QoE assessment for video frame rate and frame resolution reduction

Video bit rate reduction is very important for all video streaming applications. One possibility involves quantization domain and the majority of the work devoted to bit rate reduction focuses on this aspect only. The other possibility is to modify a video in time or space domain i.e. change the frames per second FPS rate or frame resolution FR. In this paper we present two no reference metrics mapping FPS rate and FR into MOS (Mean Opinion Scale). The performance of both models is significantly improved by incorporating content characteristics such as spatial information SI and temporal information TI. The impact on Quality of Experience (QoE) of both content characteristics is discussed with relation to the FPS rate and FR changes and general conclusions are drawn. The models were estimated and verified upon results of subjective experiments performed using video sequences of diverse spatial and temporal variability. The considered FPS rate was changed from 5 to 30 and the considered FR was changed from SQCIF to SD.

A no reference metric for the quality assessment of videos affected by exposure distortion

In the field of still and moving pictures digital imaging one of the most important factors affecting the quality of the acquisition is a correct exposure. This factor is especially important for video surveillance. It is particularly applicable in the case of CCTV cameras often operating in low-light conditions or blinded by an external light source. It is quite common that the lighting conditions are well below the adaptive capacity of a camera. Therefore, it is necessary to continuously monitor the level of exposure to determine image quality. It should be noted that the level of exposure is not expressed using quantitative parameters of acquisition (being sometimes meaningless from the perceived quality assessment point of view) but is measured directly on the image. The purpose of the presented research was to develop a No Reference (NR) metric assessing video Quality of Experience (QoE) affected with the exposure distortion. It was presumed that both over- and under-exposure degrade QoE, so the research task was to derive a proper mapping function between QoE and exposure. The presented exposure metric is unique and no similar research was found in the literature.