Donghui Wang

A model of visual attention detection based on phase spectrum

Visual attention detecting has drawn great research interest due to its useful application in the area of multimedia. Recently, a fast spectral residual approach in frequency domain was proposed to find the Region of Interest in an image. It was proved to be able to give better performance than the classic approaches. In this paper, we propose a new model for Region of Interest detection based on the phase spectrum. First, we analyze the reason that the salient region can create “pop-out” in the feature maps of the phase spectrum in spatial domain. Second, in order to obtain better feature maps, we propose to blur the image and then get the feature maps based on the phase spectrum of the blurred image. Last, we present a model to generate Region of Interest accordingly. Experimental results show that our approach achieves better and more robust results than that from the state-of-the-art.

A fuzzy logic based fast inter mode selection algorithm in H.264/AVC

H.264 adopts variable block size motion estimation and rate-distortion optimization based mode decision. They are two of the techniques that H.264 exploits to improve visual quality and achieve higher compression ratio. However, the computational complexity and encoding time are enormously increased. In this paper, a fuzzy logic based fast inter mode selection algorithm is proposed for H.264 inter prediction. With the proposed algorithm, candidate encoding modes with maximum probabilities are selected for motion estimation and unnecessary modes are cast off. Results show that about 54% on average of the total encoding time is saved with negligible quality degradation.

A PFT Visual Attention Detection Model Using Bayesian Framework

Visual attention refers to the perceptual quality that makes an object or a region pop out relative to its neighbors and seize humans visual attention. Recently, a new fast approach based on phase spectrum of Fourier Transform (PFT) was proved to be effective and also parameter-free. In this paper, we present a novel improved saliency detection model using PFT as well as the Bayesian framework. The bottom-up saliency is gathered based on PFT in several color channels and the Bayesian framework is used to incorporate top-down information with this bottom-up saliency. Experiments show that our fast PFT-based Bayesian model achieves better and more robust results than that from the state-of-the-art.