Sonia Ouni

Are Existing Procedures Enough? Image and Video Quality Assessment: Review of Subjective and Objective Metrics

Images and videos are subject to a wide variety of distortions during acquisition, digitizing, processing, restoration, compression, storage, transmission and reproduction, any of which may result in degradation in visual quality. That is why image quality assessment plays a major role in many image processing applications. Image and video quality metrics can be classified by using a number of criteria such as the type of the application domain, the predicted distortion (noise, blur, etc.) and the type of information needed to assess the quality (original image, distorted image, etc.). In the literature, the most reliable way of assessing the quality of an image or of a video is subjective evaluation [1], because human beings are the ultimate receivers in most applications. The subjective quality metric, obtained from a number of human observers, has been regarded for many years as the most reliable form of quality measurement. However, this approach is too cumbersome, slow and expensive for most applications [2]. So, in recent years a great effort has been made towards the development of quantitative measures. The objective quality evaluation is automated, done in real time and needs no user interaction. But ideally, such a quality assessment system would perceive and measure image or video impairments just like a human being [3]. The quality assessment is so important and is still an active and evolving research topic because it is a central issue in the design, implementation, and performance testing of all systems [4, 5]. Usually, the relevant literature and the related work present only a state of the art of metrics that are limited to a specific application domain. The major goal of this paper is to present a wider state of the art of the most used metrics in several application domains such as compression [6], restoration [7], etc. In this paper, we review the basic concepts and methods in subjective and objective image/video quality assessment research and we discuss their performances and drawbacks in each application domain. We show that if in some domains a lot of work has been done and several metrics were developed, on the other hand, in some other domains a lot of work has to be done and specific metrics need to be developed.

No-reference Image Semantic Quality Approach using Neural Network

Assessment for image quality traditionally needs its original image as a reference but the most of time it is not the case. So, No-Reference (NR) Image Quality Assessment (IQA) seeks to assign quality scores that are consistent with human perception but without an explicit comparison with the reference image. Unfortunately, the field of NR IQA has been largely unexplored. This paper presents a new NR Image Semantic Quality Approach (NR-ISQA) that employs adaptive Neural Networks (NN) to assess the semantic quality of image color. This NN measures the quality of an image by predicting the mean opinion score (MOS) of human observer, using a set of proposed key features especially to describe color. This challenging issues aim at emulating judgment and replacing very complex and time-consuming subjective quality assessment. Two variants of our approach are proposed: the direct and the progressive of the overall quality image. The results show the performances of the proposed approach compared with the human performances.

Fuzzy VA-Files for multi-label image annotation based on visual content of regions

In this work, we propose an efficient image annotation approach based on visual content of regions. We assume that regions can be described using low-level features as well as high-level ones. Indeed, given a labeled dataset, we adopt a probabilistic semantic model to capture relationships between low-level features and semantic clusters of regions. Moreover, since most previous works on image annotation do not deal with the curse of dimensionality, we solve this problem by introducing a fuzzy version of the Vector Approximation Files (VA-Files). Indeed, the main contribution of this work resides in the association of the generative model with fuzzy VA-Files, which offer an accurate multi-dimensional indexing, to estimate relationships between low-level features and semantic concepts. In fact, the proposed approach reduces the computation complexity while optimizing the annotation quality. Preliminary experiments highlight that the suggested approach outperforms other state-of-the-art approaches.