# A Novel Adaptive Image Data Hiding and Encryption Scheme Using Constructive Image Abstraction
## 1 Introduction  
Image abstraction transforms an input image into a new image with a distinct style while preserving the contents in the original image. The goal of image abstraction is to create visually appealing images with specific aesthetics, such as impressionism, cubism, or surrealism. This technique is useful to make images more visually appealing for social media or advertising. [PERSON] et al. [1] introduced a framework for automated and real-time video abstraction which employs Gaussian filters and bilateral filters. [PERSON] et al. [2] proposed a quadruple-cycle framework to support iterative learning to achieve restorable arbitrary style transfer. A new quantitative evaluation method was introduced to measure content preservation and style embedding performance, while solving the content leak problem from an image restoration perspective. However, these methods consider the input image as a color image, also known as low-dynamic-range (LDR) image, as the pixel values are within the fixed range of [0, 1], before scaling up to [0, 255] for storage purpose. Consequently, these image abstraction algorithms are not applicable to high-dynamic-range (HDR) images which usually exceed the fixed range. HDR images [3, 4] have become increasingly popular due to their ability to offer a wider dynamic range. Dynamic range refers to the range of brightness or luminance values that are present in the image. HDR images offer improved image detail and a closer approximation to human visual perception compared to traditional LDR images. RGBE [5], LogLuv including LogLuv24 and LogLuv32 [6], and OpenEXR [7] are three major established HDR image file formats. In contrast to the integer representation in LDR images, HDR images are able to precisely represent a broad range of luminance and colorsby utilizing floating-point numbers to store critical information. This unique capability positions HDR images as a promising candidate to become the dominant image standard in the future. In particular, high-dynamic-range imaging [8] represents a technique for accurately representing a wide range of intensity levels captured in real scenes from an extensive dynamic range of exposures; it has been intensively investigated with applications in image processing, computer graphics, and computer vision. In contrast to high-dynamic-range imaging, only a limited number of HDR image abstraction techniques have been proposed. [PERSON] et al. [9] modified the image filtering scheme of [1] to solve the image abstraction transfer problem and successfully abstracted HDR images. [PERSON] et al. [10] introduced an integrated filter-based approach to produce the effect of line drawing from HDR images; their scheme incorporates the features of anisotropic filter, shock filter with iterations, and bilateral filter to produce the important shapes in HDR images without upsetting the human visual effect. Regarding data hiding for HDR images, several studies proposed an adaptive data hiding approach to balance image quality and embedding rate. These approaches leverage the fact that HDR images often have a wide range of luminance and colors, while the human eye has different sensitivity in different luminance and texture. [PERSON] and [PERSON] [11] pioneered these works and pixels in RGBE images are categorized into homogeneous or heterogeneous areas to carry adjustable secret data. They offered an embedding rate ranging from 5.13 to 9.69 bits per pixel (bpp); however, the peak signal-to-noise ratios (PSNR) of the resultant tone-mapped stego images were only slightly greater than 30 dB. By exploiting the homogeneous representations of RGBE pixels, [PERSON] et al. [12] concealed different numbers of messages based on the homogeneous representations and achieved data hiding without distortion in HDR RGBE images, but their scheme has to pay the penalty of providing a limited embedding rate in the range of 0.127-0.145 bpp. [PERSON] et al.