A computational model of visual attention.

Visual attention is a process by which the Human Visual System (HVS) selects most important information from a scene. Visual attention models are computational or mathematical models developed to predict this information. The performance of the state-of-the-art visual attention models is limited in terms of prediction accuracy and computational complexity. In spite of significant amount of active research in this area, modelling visual attention is still an open research challenge. This thesis proposes a novel computational model of visual attention that achieves higher prediction accuracy with low computational complexity. A new bottom-up visual attention model based on in-focus regions is proposed. To develop the model, an image dataset is created by capturing images with in-focus and out-of-focus regions. The Discrete Cosine Transform (DCT) spectrum of these images is investigated qualitatively and quantitatively to discover the key frequency coefficients that correspond to the in-focus regions. The model detects these key coefficients by formulating a novel relation between the in-focus and out-of-focus regions in the frequency domain. These frequency coefficients are used to detect the salient in-focus regions. The simulation results show that this attention model achieves good prediction accuracy with low complexity. The prediction accuracy of the proposed in-focus visual attention model is further improved by incorporating sensitivity of the HVS towards the image centre and the human faces. Moreover, the computational complexity is further reduced by using Integer Cosine Transform (ICT). The model is parameter tuned using the hill climbing approach to optimise the accuracy. The performance has been analysed qualitatively and quantitatively using two large image datasets with eye tracking fixation ground truth. The results show that the model achieves higher prediction accuracy with a lower computational complexity compared to the state-of-the-art visual attention models. The proposed model is useful in predicting human fixations in computationally constrained environments. Mainly it is useful in applications such as perceptual video coding, image quality assessment, object recognition and image segmentation

A low complexity visual saliency model based on in-focus regions and centre sensitivity.

A novel low-complexity visual saliency detection algorithm for detecting salient regions in images is proposed. The algorithm derives salient regions based on in-focus regions and image centre sensitivity. The performance of the algorithm in predicting human eye fixations is validated against ten state-of-the-art algorithms using a public image dataset. The results demonstrate that the proposed algorithm achieves higher prediction accuracy in saliency detection at significantly lower computational complexity compared to other algorithms

Investigation of the effectiveness of video quality metrics in video pre-processing.

This paper presents an investigation of the effectiveness of current video quality measurement metrics in measuring variations in perceptual video quality of pre-processed video. The results show that full-reference video quality metrics are not effective in detecting variations in perceptual video quality. However, no reference metrics show better performance when compared to full reference metrics, particularly, Naturalness Image Quality Evaluator (NIQE) is notably better at detecting perceptual quality variations

A DCT based in-focus visual saliency detection algorithm.

A novel low-complexity visual saliency detection algorithm for detecting visually salient regions in images based on camera focus is presented. This fast in-focus region detection algorithm detects salient-frequencies present in in-focus areas using the characteristics of Discrete Cosine Transform coefficients. The performance of this algorithm is validated against five state-of-the-art saliency detection algorithms. The results show that this algorithm consistently outperforms other saliency detection algorithms in terms of complexity and prediction accuracy