04-02-2013, 10:20 AM
URINE ACTIVATED PAPER BATTERIES FOR BIOSYSTEMS
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ABSTRACT
A spatial noise shaping (SNS) method based on human visual
sensitivity is presented. The method exploits the capability of frequency domain linear prediction for spatial envelope retrieval. It effectively shapes (or hides) the noise of an image in areas which are not sensitive to human vision so that the resultingimage is more pleasant to human eyes. The noise comes from the processing of the image, and it can be either separable like the additive noise pattern in image watermarking or non
separable like the quantization noise in image coding. An application of the algorithm is demonstrated in the paper by using it to enhance image coders. Images decoded from the SNS incorporated coders have superior perceived quality than those without using SNS.
INTRODUCTION
NOISE appearance has always been unwelcome in many speech, audio, and image applications. Many studies have been done in the field of noise reduction, ranging from conventional median or Wiener filter types of algorithms [1] to recent wavelet denoising techniques [2], [3]. Although these methods are able to eliminate or reduce the amount of noise, some useful information in the host signal may be damaged by them as well, and the damage is usually proportional to the amount of noise reduced. This tradeoff constitutes the major challenge for these methods, and limits their usage.
SNS BASED ON HUMAN VISUAL SENSITIVITY
The human visual system (HVS) is more sensitive to detecting noise in flat (smooth) regions than it is in textural regions, that is, texture and edge areas have bigger masking capability with respect to noise. In other words, the sensitivity of the human visual
system is proportional to the flatness of the image, that is, the rougher the image, the more masking. In order to make the most of these locally space-dependent masking thresholds, we have to shape the coding noise according to the roughness (textures and edges) of the image, not to the overall image amplitude envelope. This can be accomplished by first weighting the spectral coefficients before deriving the LPC.
CONCLUSION
A generic SNS technique for images is proposed for hiding quantization (or processing related) noise in areas which are not sensitive to human visual perception. A solution to resolve the stability issue of 2-D linear prediction is also presented. The algorithm is applicable to various tasks and the application for enhancing image coders is demonstrated. SNS can be used as embedded or ad-hoc processing. For coding typical images like Lena which is a combination of flat, edge and texture regions, SNS can effectively hide the coding noise in perceptually insensitive areas (such as texture and edges) and at the same time preserve sharp edges. Hence, given the compression rate, images produced by the coder with SNS possesses a superior perceived quality than those produced by the coder without SNS.