Studies on Properties of HVS for Ring Detection
N. V. K. Mahalakshmi, Vedala Naga Sailaja, K. Jeevana Jyothi · 2012
In this paper, I observed the properties of HVS for Ring Detection. The automatic detection of regions visually impaired by ringing artifacts in compressed images, it is a no-reference approach, taking into account the specific physical structure of ringing artifacts combined with properties of the human visual system (HVS). The approach is validated with the results of a psycho visual experiment, and its performance is compared to existing alternatives in literature for ringing region detection. Experimental results show that our method is appropriate in terms of both reliability and computational efficiency. I. Introduction The occurrence of the compression induced artifacts depends on the data source, target bit rate, and underlying compression scheme and their visibility can range from imperceptible to very annoying, thus affecting perceived quality. Research on the design of blockiness metric has shown that an efficient no-reference approach intrinsically exists of two steps: 1) the detection of regions in an image where blockiness might occur, and 2) the determination of the blocking annoyance in these regions. We use a similar two-step approach for the design of a no-reference ringing metric. This paper only discusses the first step: the detection of regions in the image, in which visible ringing occurs. This, however, does not always reflect human visual perception of ringing, because of the absence of spatial masking as typically present in the HVS. This issue is taken into account by incorporating properties of the HVS into the detection method. Obviously, the optimal performance in terms of reducing the number of required computations, while maintaining the reliable detection of perceived ringing, can be achieved by optimizing two aspects: 1) the detection accuracy of relevant edges; and 2) the reduction in complexity of the HVS model itself. Hence, what is needed is an edge detector that only extracts edges most closely related to the occurrence of ringing, and a HVS model that is simpler (and thus more applicable for real time implementation) than the approaches existing in literature.