Kafri and Keren於1987年提出一個像素不擴展視覺密碼方法，稱之為隨機網格視覺密碼。隨機網格視覺密碼的相關研究大部分都是以產生雜訊式(noise-like)的分享影像為主，鮮少為有意義的分享影像研究。在這些少數的有意義隨機網格的研究裡，有諸多的使用限制，且都無法讓分享影像和疊合影像上的色差達到理論上的最大值，造成影像視覺品質的下降。
本研究分別討論有意義式的分享方法與雜訊式的分享方法，分析了他們在分享影像與疊合影像上黑點比率的關係，找出能在疊合影像上產生最佳對比色差的黑點機率配置方法，藉此提出一個以機率為基礎的有意義視覺密碼模型。我們的方法降低了使用上的限制，且可以實作出多種色差對比的影像。最重要的是提升了影像的視覺品質，讓分享影像與疊合影像的對比色差都可以達到理論上的最大值，優於其他的相關研究。

Kafri and Keren presented a visual secret sharing method without pixel expansion in 1987, called random-grid visual secret sharing (RGVSS). However, most RGVSS research has limitations, such as generating noise-like shares rather than generating meaningful shares, creating many restrictions in encoding secret and degrading image contrast.
This study will discuss the probability-based visual secret sharing methods for generating both meaningful and noise-like shares. We first analyzed the distribution of the black pixels on the share-image and the stack-image. A probability allocation method was then proposed which could produce the best contrast in both of the share-image and the stackimage. Our meaningful module not only uses different cover images for disguised, but also has the ability for setting a variety of contrasts as we needed. The most important result is that this study raises the visual quality to their theoretical maximum for both of the share-image and the stack-image. To conclude, our meaningful visual secret sharing module is superior to other related research.