CLASSIFICATION OF BIT-PLANE NOISE ON IMAGES USING FUZZY LOGIC
Abstract
BPCS (Bit-Plane Complexity Segmentation) is a fairly new steganography technique The most important process in BPCS is the calculation of complexity value of a bit-plane. The bit-plane complexity is calculated by looking at how much of the change bits contained in a bit-plane. If the bit-plane, has a high complexity, the bi-plane is categorized as a bit-plane noise that does not contain valuable information on the image. Classification bit-plane using the set cripst set (noise/not) is not fair, where a little difference of the value will significantly change the status of the bit-plane. This study attempts to apply the principles of fuzzy sets to classify the bit-plane into into three sets are informative, informative part, and the noise region. Classification bit-plane into a fuzzy set stretcher expected to classify the bit-plane is more objective and ultimately the capacity of images to the message can be improved by using the fuzzy inference mamdani to take decisions which bit-plane will be replaced with a message based on the classification of bit-plane and the size of the message that will be inserted.
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