On Evaluating Video Object Segmentation Quality: A Perceptually driven Objective Metric

Segmentation of moving objects in image sequences plays an important role in video processing and analysis. Evaluating the quality of segmentation results is necessary to allow the appropriate selection of segmentation algorithms and to tune their parameters for optimal performance. Many segmentation algorithms have been proposed along with a number of evaluation criteria. Nevertheless, no formal psychophysical experiments evaluating the quality of different video object segmentation results have been conducted. In this paper, a generic framework for segmentation quality evaluation is presented. A perceptually driven automatic method for segmentation evaluation is proposed and compared against state-of-the-art. Moreover, on the basis of subjective results, weighting strategies are introduced into the proposed objective metric to meet the specificity of different segmentation applications such as video compression and mixed reality. Experimental results confirm the efficiency of the proposed approach

On Evaluating Video Object Segmentation Quality: A Perceptually Driven Objective Metric

The task of extracting objects in video sequences emerges in many applications such as object-based video coding (e.g., MPEG-4) and content-based video indexing and retrieval (e.g., MPEG-7). The MPEG-4 standard provides specifications for the coding of video objects, but does not address the problem of how to extract foreground objects in image sequences. Therefore, for specific applications, evaluating the quality of foreground/background segmentation results is necessary to allow for an appropriate selection of segmentation algorithms and for tuning their parameters for optimal performance. Many segmentation algorithms have been proposed along with a number of evaluation criteria. Nevertheless, formal psychophysical experiments evaluating the quality of different video foreground object segmentation results have not yet been conducted. In this paper, a generic framework for both subjective and objective segmentation quality evaluation is presented. An objective quality assessment method for segmentation evaluation is derived on the basis of perceptual factors through subjective experiments. The performance of the proposed method is shown on different state-of-the-art foreground/background segmentation algorithms and our method is compared to other objective methods which do not include perceptual factors. Moreover, on the basis of subjective results, weighting strategies are introduced into the proposed metric to meet the specificity of different segmentation applications e.g., video compression, video surveillance and mixed reality. Experimental results confirm the efficiency of the proposed approach

A Framework for Evaluating Video Object Segmentation Algorithms

Segmentation of moving objects in image sequences plays an important role in video processing and analysis. Evaluating the quality of segmentation results is necessary to allow the appropriate selection of segmentation algorithms and to tune their parameters for optimal performance. Many segmentation algorithms have been proposed along with a number of evaluation criteria. Nevertheless, no psychophysical experiments evaluating the quality of different video object segmentation results have been conducted. In this paper, a generic framework for segmentation quality evaluation is presented. A perceptually driven automatic method for segmentation evaluation is proposed and compared against an existing approach. Moreover, on the basis of subjective results, perceptual factors are introduced into the novel objective metric to meet the specificity of different segmentation applications such as video compression. Experimental results confirm the efficiency of the proposed evaluation criteria

No-reference video quality assessment model based on artifact metrics for digital transmission applications

Tese (doutorado)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2017.Um dos principais fatores para a redução da qualidade do conteúdo visual, em sistemas de imagem digital, são a presença de degradações introduzidas durante as etapas de processamento de sinais. Contudo, medir a qualidade de um vídeo implica em comparar direta ou indiretamente um vídeo de teste com o seu vídeo de referência. Na maioria das aplicações, os seres humanos são o meio mais confiável de estimar a qualidade de um vídeo. Embora mais confiáveis, estes métodos consomem tempo e são difíceis de incorporar em um serviço de controle de qualidade automatizado. Como alternativa, as métricas objectivas, ou seja, algoritmos, são geralmente usadas para estimar a qualidade de um vídeo automaticamente. Para desenvolver uma métrica objetiva é importante entender como as características perceptuais de um conjunto de artefatos estão relacionadas com suas forças físicas e com o incômodo percebido. Então, nós estudamos as características de diferentes tipos de artefatos comumente encontrados em vídeos comprimidos (ou seja, blocado, borrado e perda-de-pacotes) por meio de experimentos psicofísicos para medir independentemente a força e o incômodo desses artefatos, quando sozinhos ou combinados no vídeo. Nós analisamos os dados obtidos desses experimentos e propomos vários modelos de qualidade baseados nas combinações das forças perceptuais de artefatos individuais e suas interações. Inspirados pelos resultados experimentos, nós propomos uma métrica sem-referência baseada em características extraídas dos vídeos (por exemplo, informações DCT, a média da diferença absoluta entre blocos de uma imagem, variação da intensidade entre pixels vizinhos e atenção visual). Um modelo de regressão não-linear baseado em vetores de suporte (Support Vector Regression) é usado para combinar todas as características e estimar a qualidade do vídeo. Nossa métrica teve um desempenho muito melhor que as métricas de artefatos testadas e para algumas métricas com-referência (full-reference).The main causes for the reducing of visual quality in digital imaging systems are the unwanted presence of degradations introduced during processing and transmission steps. However, measuring the quality of a video implies in a direct or indirect comparison between test video and reference video. In most applications, psycho-physical experiments with human subjects are the most reliable means of determining the quality of a video. Although more reliable, these methods are time consuming and difficult to incorporate into an automated quality control service. As an alternative, objective metrics, i.e. algorithms, are generally used to estimate video quality quality automatically. To develop an objective metric, it is important understand how the perceptual characteristics of a set of artifacts are related to their physical strengths and to the perceived annoyance. Then, to study the characteristics of different types of artifacts commonly found in compressed videos (i.e. blockiness, blurriness, and packet-loss) we performed six psychophysical experiments to independently measure the strength and overall annoyance of these artifact signals when presented alone or in combination. We analyzed the data from these experiments and proposed several models for the overall annoyance based on combinations of the perceptual strengths of the individual artifact signals and their interactions. Inspired by experimental results, we proposed a no-reference video quality metric based in several features extracted from the videos (e.g. DCT information, cross-correlation of sub-sampled images, average absolute differences between block image pixels, intensity variation between neighbouring pixels, and visual attention). A non-linear regression model using a support vector (SVR) technique is used to combine all features to obtain an overall quality estimate. Our metric performed better than the tested artifact metrics and for some full-reference metrics

A hybrid no-reference video quality metric for digital transmission applincatios

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2017.Este trabalho visa desenvolver uma métrica híbrida de qualidade de vídeo sem referência para aplicações de transmissão digital, que leva em consideração três tipos de artefatos: perda de pacotes, blocado e borrado. As características desses artefatos são extraídas a partir das sequências de vídeo a fim de quantificar a força desses artefatos. A avaliação de perda de pacotes é dividida em 2 etapas: detecção e medição. As avaliações de blocado e borrado seguem referências da literatura. Depois de obter as características dos três tipos de artefatos, um processo de aprendizado de máquina (SVR) é utilizado para estimar a nota de qualidade prevista a partir das características extraídas. Os resultados obtidos com a métrica proposta foram comparados com os resultados obtidos com outras três métricas disponíveis na literatura (duas métricas NR de perda de pacotes e 1 métrica FR) e eles são promissores. A métrica proposta é cega, rápida e confiável para ser usada em cenários em tempo real.This work aims to develop a hybrid no-reference video quality metric for digital transmission applications, which takes into account three types of artifacts: packet-loss, blockiness and bluriness. Features are extracted from the video sequences in order to quantity the strength of these three artifacts. The assessment of the packet-loss strength is performed in 2 stages: detection and measurement. The assessment of the strength of blockiness and blussiness follow references from literature. After obtaining the features from these three types of artifacts, a machine learning algorithm ( the support vector regression technique), is used to estimate the predicted quality score from the extracted features. The results obtained with the proposed metric were compared with the results obtained with three other metrics available in the literature (two NR packet-loss metrics and one FR metric). The proposed metric is blind, fast, and reliable to be used in real-time scenarios

Statistical Multiplexing of H.264 programms

The advent of H.264/AVC is going to change the way Digital Television programs are broadcast. Each program can be independently encoded or jointly encoded resulting thus in a more efficient way to distribute the available channel bandwidth. This paper presents a combined coding scheme for multi-program video transmission in which the channel capacity is distributed among the programs according to the program complexities. A complexity bit rate control algorithm based on the Structural Similarity Index (SSIM) is proposed. SSIM metric is presented under the hypothesis that the Human Visual System (HSV) is very specialized in extracting structural information from a video sequence but not in extracting the errors. Thus, a measurement on structural distortion should give a better correlation to the subjective impression. Current simulations have demonstrated very promising results showing that the algorithm can effectively control the complexity of the multi-program encoding process whilst improving overall subjective