14 April 2000 Model observer based optimization of JPEG image compression
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Abstract
Recent work applied model observers to predict the effect of JPEG and wavelet image compression on human visual detection of simulated lesions embedded in real structured backgrounds (x-ray coronary angiograms). We extend the use of model observers to perform parameter optimization of image compression in order to maximize visual detection performance at a given compression ratio. A simulated annealing algorithm was used to find the optimal quantization matrix. In each iteration, the 64 quantization parameters of the JPEG algorithm were randomly perturbed (while preserving a fixed compression ratio). Each set of quantization parameters setting was used to compress 400 test images. Model observer performance (Pc) was then obtained for the images that had undergone compression. The simulated annealing algorithm converged (as determined by the 'annealing schedule') to an 'optimal' quantization matrix. A follow-up human psychophysical study with two naive observers was conducted to compare optimal quantization matrix with respect to the default JPEG quantization matrix. For both human observers visual detection performance improved significantly from the default to the optimized quantization matrix condition. Our results suggest that model observers can be successfully used for task-based performance optimization of image compression algorithms.
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Miguel P. Eckstein, Craig K. Abbey, Jay L. Bartroff, "Model observer based optimization of JPEG image compression", Proc. SPIE 3981, Medical Imaging 2000: Image Perception and Performance, (14 April 2000); doi: 10.1117/12.383096; https://doi.org/10.1117/12.383096
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