# A Novel Adaptive Image Data Hiding and Encryption Scheme Using Constructive Image Abstraction
## 2 Our Proposed Methods
### Image Abstraction  
The process of image abstraction involves taking the original image \(I\) as input, and transforming it into a new image with a different style while preserving the original content. However, rather than outputting the converting result as an image, we use the intermediate abstracted image \(I_{A}\) as the input for the succeeding steps.  
Figure 1a depicts the flowchart of our image abstraction algorithm. The algorithm comprises five distinct steps. The first step is image luminance scaling and leveling (ILSL). This process can appropriately scale the luminance of HDR images for reducing the information of HDR images. After abstracting an image, the luminance of the abstracted image will be reconstructed near the magnitude of the original HDR images. In addition, this process can also be used for LDR images because they have fixed luminance range, which can be considered as a special case of an HDR image.  
The second step is image information reduction (IIR). In this phase, we present a new trilateral edge-preserving filter based on the bilateral filter. This new filter can reduce the redundant information within an image and effectively maintain edge information within an image than the existent bilateral and trilateral filters. Through this step, the filtered result can present better gradations between objects. Note that, in an image abstraction algorithm, the effect of the filter will influence the final abstracted results.  
The third step of image abstraction is image luminance quantization (ILQ). In general, cartoonists have long employed the various color blocks to represent different lighting distributions. To accomplish such an effect, we modify the video abstraction model, encouraging our algorithm to represent the lighting distributions of an image more effectively, making the output image more similar to that of the original image. It also provides an alternative choice for end users to refine their work. Further, differing from [1], our method is capable of automatically determining the suitable luminance level, depending on the luminance distribution appearance in an image.  
The next step of image abstraction is image edge detection (IED). Cartoon-based images usually employ some obvious edges to simply exhibit the contour of an object. To simulate this characteristic, we first employ the Difference of Gaussian (DoG) filter to determine the edge information of a target object. We then combine the edge information with the abstracted result, making the abstracted image demonstrate a more visually pleasant appearance.  
Figure 1: The flowchart of our method. (**a**) The image abstraction process. (**b**) The data embedding and encryption process. Note that the intermediate abstracted image, \(I_{A}\), is not produced in our scheme due to the constructive data embedding approach; instead, we exhibit it here to clarify the subsequent stages. (**c**) The image decryption and data extraction process.  
The final step is fuzzy-relationship-based image composition (FIC). In this step, we balance the influences of both abstracted image and quantized images by computing their fuzzy relationship. The output of these five steps are abstracted images with black contour highlighting the objects ready for data concealment.