We present a novel algorithm for enhancing an image or video frame with depth of field. The algorithm deals with occlusive blurring effects as occur in real cameras and can handle a variation in blur which is continuous up to blur level quantization granularity, with asymptotic complexity O(N log2N) in terms of time and memory for an N-pixel image, irrespective of the complexity of the variation in blur. The proposed algorithm is a postfiltering approach which, unlike prior algorithms, does not suffer from intensity leakage. Experimental results show the algorithm to be 3 to 4 times faster than an existing algorithm, which had the same asymptotic complexity.

We introduce the concept of depth-based blurring to achieve an aesthetically acceptable distortion when reducing the bitrate in image coding. The proposed depth-based blurring is a prefiltering that reduces high-frequency components by mimicking the limited depth of field effect that occurs in cameras. To cope with the challenge of avoiding intensity leakage at the boundaries of objects when blurring at different depth levels, we introduce a selective blurring algorithm that simulates occlusion effects as occur in natural blurring. The proposed algorithm can handle any number of blurring and occlusion levels. Subjective experiments show that the proposed algorithm outperforms foveation filtering, which is the dominant approach for bitrate reduction by space-variant prefiltering.

This paper presents a computationally efficient algorithm for smoothly space-variant Gaussian blurring of images. The proposed algorithm uses a specialized filter bank with optimal filters computed through principal component analysis. This filter bank approximates perfect space-variant Gaussian blurring to arbitrarily high accuracy and at greatly reduced computational cost compared to the brute force approach of employing a separate low-pass filter at each image location. This is particularly important for spatially variant image processing such as foveated coding. Experimental results show that the proposed algorithm provides typically 10 to 15 dB better approximation of perfect Gaussian blurring than the blended Gaussian pyramid blurring approach when using a bank of just eight filters.

We present a novel selective blurring algorithm that mimics the optical distance blur effects that occur naturally in cameras and eyes. The proposed algorithm provides a realistic simulation of distance blurring, with the desirable properties of aiming to mimic occlusion effects as occur in natural blurring, and of being able to handle any number of blurring and occlusion levels with the same order of computational complexity. We have performed subjective experiments to compare the perceived quality of distance blurred images with that of foveation-filtered images under equivalent conditions, when both are used as space-variant prefiltering stages prior to a JPEG encoder. The results show that the distance-based blurring was significantly preferable to the foveation blurring for four out of nine test images, whereas a significant converse preference was found for only one test image.

We present a method for computing a function of average multi-viewer eye sensitivity based on the Geisler & Perry contrast threshold formula, and, from this, the cut-off frequency map (as used in foveation filtering) that is optimal in the sense of discarding frequencies in least-noticeable-first order. Existing approaches usually solve the multi-viewer foveation problem as a number of single-viewer foveations, effectively taking collective sensitivity to be the maximum of the individual viewer eye sensitivities. This has inherent problems such as over-sensitivity to outliers which are not problems with the proposed approach. Furthermore, the proposed approach can be employed in the infinite-viewer (probability-based) scenario without additional cost.