Hocine Cherifi

Sporadic frame dropping impact on quality perception

Over the past few years there has been an increasing interest in real time video services over packet networks. When considering quality, it is essential to quantify user perception of the received sequence. Severe motion discontinuities are one of the most common degradations in video streaming. The end-user perceives a jerky motion when the discontinuities are uniformly distributed over time and an instantaneous fluidity break is perceived when the motion loss is isolated or irregularly distributed. Bit rate adaptation techniques, transmission errors in the packet networks or restitution strategy could be the origin of this perceived jerkiness. In this paper we present a psychovisual experiment performed to quantify the effect of sporadically dropped pictures on the overall perceived quality. First, the perceptual detection thresholds of generated temporal discontinuities were measured. Then, the quality function was estimated in relation to a single frame dropping for different durations. Finally, a set of tests was performed to quantify the effect of several impairments distributed over time. We have found that the detection thresholds are content, duration and motion dependent. The assessment results show how quality is impaired by a single burst of dropped frames in a 10 sec sequence. The effect of several bursts of discarded frames, irregularly distributed over the time is also discussed.

Comparative evaluation of community detection algorithms: a topological approach

Community detection is one of the most active fields in complex network analysis, due to its potential value in practical applications. Many works inspired by different paradigms are devoted to the development of algorithmic solutions allowing the network structure in such cohesive subgroups to be revealed. Comparative studies reported in the literature usually rely on a performance measure considering the community structure as a partition (Rand index, normalized mutual information, etc). However, this type of comparison neglects the topological properties of the communities. In this paper, we present a comprehensive comparative study of a representative set of community detection methods, in which we adopt both types of evaluation. Community-oriented topological measures are used to qualify the communities and evaluate their deviation from the reference structure. In order to mimic real-world systems, we use artificially generated realistic networks. It turns out there is no equivalence between the two approaches: a high performance does not necessarily correspond to correct topological properties, and vice versa. They can therefore be considered as complementary, and we recommend applying both of them in order to perform a complete and accurate assessment.

Hypergraph imaging: an overview

Hypergraph theory as originally developed by Berge (Hypergraphe, Dunod, Paris, 1987) is a theory of finite combinatorial sets, modeling lot of problems of operational research and combinatorial optimization. This framework turns out to be very interesting for many other applications, in particular for computer vision. In this paper, we are going to survey the relationship between combinatorial sets and image processing. More precisely, we propose an overview of different applications from image hypergraph models to image analysis. It mainly focuses on the combinatorial representation of an image and shows the effectiveness of this approach to low level image processing; in particular to segmentation, edge detection and noise cancellation.

Qualitative Comparison of Community Detection Algorithms

Community detection is a very active field in complex networks analysis, consisting in identifying groups of nodes more densely interconnected relatively to the rest of the network. The existing algorithms are usually tested and compared on real-world and artificial networks, their performance being assessed through some partition similarity measure. However, artificial networks realism can be questioned, and the appropriateness of those measures is not obvious. In this study, we take advantage of recent advances concerning the characterization of community structures to tackle these questions. We first generate networks thanks to the most realistic model available to date. Their analysis reveals they display only some of the properties observed in real-world community structures. We then apply five community detection algorithms on these networks and find out the performance assessed quantitatively does not necessarily agree with a qualitative analysis of the identified communities. It therefore seems both approaches should be applied to perform a relevant comparison of the algorithms.

Maximum likelihood difference scaling of image quality in compression-degraded images

Lossy image compression techniques allow arbitrarily high compression rates but at the price of poor image quality. We applied maximum likelihood difference scaling to evaluate image quality of nine images, each compressed via vector quantization to ten different levels, within two different color spaces, RGB and CIE 1976 L*a*b*. In L*a*b* space, images could be compressed on average by 32% more than in RGB space, with little additional loss in quality. Further compression led to marked perceptual changes. Our approach permits a rapid, direct measurement of the consequences of image compression for human observers.

Combinatorics and image processing

In this paper, we introduce an image combinatorial model based on hypergraph theory. Hypergraph theory is an efficient formal frame for developing image processing applications such as segmentation. Under the assumption that a hypergraph satisfies the Helly property, we develop a segmentation algorithm that partitions the image by inspecting packets of pixels. This process is controlled by a homogeneity criterion. We also present a preprocessing algorithm that ensures that the hypergraph associated with any image satisfies the Helly property. We show that the algorithm is convergent. A performance analysis of the model and of the segmentation algorithm is included.

HOS-based image sequence noise removal

In this paper, a new spatiotemporal filtering scheme is described for noise reduction in video sequences. For this purpose, the scheme processes each group of three consecutive sequence frames in two steps: 1) estimate motion between frames and 2) use motion vectors to get the final denoised current frame. A family of adaptive spatiotemporal L-filters is applied. A recursive implementation of these filters is used and compared with its nonrecursive counterpart. The motion trajectories are obtained recursively by a region-recursive estimation method. Both motion parameters and filter weights are computed by minimizing the kurtosis of error instead of mean squared error. Using the kurtosis in the algorithms adaptation is appropriate in the presence of mixed and impulsive noises. The filter performance is evaluated by considering different types of video sequences. Simulations show marked improvement in visual quality and SNRI measures cost as well as compared to those reported in literature.

Application of Adaptive Hypergraph Model to Impulsive Noise Detection

In this paper, using hypergraph theory, we introduce an image model called Adaptive Image Neighborhood Hypergraph (AINH). From this model we propose a combinatorial definition of noisy data. A detection procedure is used to classify the hyperedges either as noisy or clean data. Similar to other techniques, the proposed algorithm uses an estimation procedure to remove the effects of the noise. Extensive simulations show that the proposed scheme consistently works well in suppressing of impulsive noise.

Temporal masking effect on dropped frames at video scene cuts

In video sequences, scene cuts produce a temporal masking effect on several kinds of artifacts. This temporal sensitivity reduction of the human visual system could be present before (backward masking) and after (forward masking) scene cuts. Related studies reported a significant forward masking in the first 30 to 100 ms following a scene change depending on the impairment nature and the picture content. Backward masking at scene cuts seems to be less significant. In this paper we present the results of a psychovisual experiment performed to characterize the temporal masking effect on discontinuities caused by dropped frames in the vicinity of scene cuts. The forward and backward masking was estimated in relation to a single burst of discarded frames of different durations. The four alternatives forced choice psychophysical method was employed to evaluate the detection thresholds. The test was carried out using natural video contents. Our results from the forward masking test are consistent with those reported in the state of the art even if the test conditions were quite different. However, the back masking effect on frame dropping perception is more significant than with forward masking.

Noise detection and cleaning by hypergraph model

This paper introduces a new algorithm for visual reconstruction of digital images which have been corrupted by mixed noise. From an image hypergraph model we introduce a combinatorial definition of noisy data. A detection procedure is used to classify the hyperedges either as noisy or clean data. Similar to other techniques, the proposed algorithm uses then an estimation procedure to remove the effects of the noise from image data. Numerical simulations demonstrate that this algorithm suppress the effect of the noise while preserving the edges with a high degree of accuracy at a relatively low computational cost.