Task relevant image content segmentation for compression
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This thesis is concerned with the automatic detection and segmentation of visually salient image regions and subsequent targeted image compression in order to maintain observer performance levels while reducing image filesize. In moving towards this goal, pertinent issues have been addressed: the viability of "black-box" frequency transmission models, statistical measures of the effect of image processing, observer perception of processed images and how computer vision "feature points" correspond to visually salient image content. We show that image feature points are distributed towards visually-salient image regions: regions that are likely to attract observer attention. This remains true even when the "task" of the observer is changed: observers performing a task generally direct their attention towards image regions naturally rich in feature points. A new algorithm based on feature points, "Visual Interest", is proposed to predict image regions attended by observers. This method segments image content likely to attract visual attention under a variety of viewing conditions: passive viewing and search-directed viewing for different observer tasks. The algorithm improves the predictive power of observer eye fixations during object search task relative to "bottom-up" models. It responds only to image content, requiring no prior machine learning, in contrast to the scientific state-of-theart which relies explicitly on object categorisation. "Visual Interest" can also be run with object recognition to refine the segmentation for a particular object-category search task to reduce the "salient" area to tighter image areas. The resultant segmentation into salient and non-salient regions is used to generate regionof- interest compressed images suitable for multi-task observer analysis. Using pre-blur of JPEG we gain 15% filesize reduction beyond global JPEG application acting on image content alone and 25% when combined with object recognition. Using JPEG2000 ROI gives reductions of down to 25% of the original filesize while achieving gain in PSNR and SSIM statistics over the ROI, with the benefit of ROI priority transmission.