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WHAT THE EYE DID NOT SEE A FUSION APPROACH TO IMAGE CODING

机译:眼睛没有看到融合的图像编码方法

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摘要

The concentration of the cones and ganglion cells is much higher in the fovea than the rest of the retina. This non-uniform sampling results in a retinal image that is sharp at the fixation point, where a person is looking, and blurred away from it. This difference between the sampling rates at the different spatial locations presents us with the question of whether we can employ this biological characteristic to achieve better image compression. This can be achieved by compressing an image less at the fixation point and more away from it. It is, however, known that the vision system employs more that one fixation to look at a single scene which presents us with the problem of combining images pertaining to the same scene but exhibiting different spatial contrasts. This article presents an algorithm to combine such a series of images by using image fusion in the gradient domain. The advantage of the algorithm is that unlike other algorithms that compress the image in the spatial domain our algorithm results in no artifacts. The algorithm is based on two steps, in the first we modify the gradients of an image based on a limited number of fixations and in the second we integrate the modified gradient. Results based on measured and predicted fixations verify our approach.
机译:中央凹中视锥细胞和神经节细胞的浓度比视网膜的其余部分高得多。这种不均匀的采样会导致视网膜图像在注视点(人注视的位置)处清晰,并使其模糊。在不同空间位置的采样率之间的差异向我们提出了一个问题,即我们是否可以利用这种生物学特性来实现更好的图像压缩。这可以通过在固定点处压缩较少的图像并使其远离图像来实现。然而,众所周知,视觉系统采用了一种以上的注视来观察单个场景,这给我们带来了将属于同一场景但表现出不同空间对比度的图像组合在一起的问题。本文提出了一种通过在梯度域中使用图像融合来组合此类图像的算法。该算法的优势在于,与其他在空间域上压缩图像的算法不同,我们的算法不会产生任何伪像。该算法基于两个步骤,第一步是基于有限数量的注视来修改图像的梯度,第二步是对修改后的梯度进行积分。基于测得的和预测的注视的结果证明了我们的方法。

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