## 1 Introduction  
With the rapid advancements and pervasiveness of smart devices with photographic capabilities, an increasing number of people now aim to take pictures and share them on the social media. Such postings become social cards and play a significant role in showcasing one's life [1]. As such, mobile photo-editing apps and collage creators have become popular [2]. The production of high-quality images requires post-processing, which requires professional photographers and other experienced technicians. The process, which is largely automated today, involves vital parameter adjustments based on image types [3]. Focusing on the aesthetic quality of images, image aesthetic processing systems (IAPS) have gained significant research attention [4].  
Notably, IAP research has been very limited thus far. Image aesthetic processing (IAP) algorithms correct and enhance image content based on composition, contrast, color saturation, and brightness settings. Some extant models perform model training and prediction on the cloud [5; 6; 7]; however, it is difficult to deploy them on mobile devices, resulting in poor performance in terms of energy consumption and latency [8]. Other studies do not consider scene category information [9; 10], which implies different aesthetic processing standards.  
Considering these issues, we try to deploy an IAP deep-learning model on a resource-constrained mobile device for model inferencing and prediction. First, lightweight neural networks must be adapted to the computing and storage limitations of mobile devices [11; 12; 13; 14].  
For this purpose, we propose a light IAPS that enhances the aesthetics of images. The IAPS integrates professional photography knowledge and aesthetic features to process images based on their categories [15; 16; 17], with minimal parameter adjustments to achieve maximum enhancement effects. The significance and potential applications of this study are illustrated in Figure 1.  
The main contributions of the proposed system are summarized as follows.  
* A lightweight IAPS with intelligent scene perception, which automatically aesthetically processes images and reduces the computing burdens of resource-constrained mobile and edge devices.
* The ShuffleNet scene recognition model, which uses the lightweight TensorFlow Lite computer vision toolkit to predict scene categories.
* An automated classification-based aesthetic processing method that verifiably enhances the visual appeal of images.
* A novel contrast and color saturation model that leverages GPUImage filters to perform specific adjustments to image attributes.
* A local mean-square error filter algorithm and log curve procedure that achieves unsurpassed skin smoothing and whitening effects on human features.  
The inclusion of acronyms is illustrated in Table 1 to enhance the readability of the study.  
The remainder is organized as follows. Section 2 provides a review of related studies. Section 3 explains the design and training of the proposed system model, while Section 4 describes the model's lighting. In Section 5, we outline the realization process of multi-attribute image aesthetic processing. Next, in Section 6, we present a functional  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Complete Words** & **Acronyms** \\ \hline image aesthetics processing & IAP \\ IAP system & IAPS \\ aesthetic visual analysis & AVA \\ contrast-changed image database & CCID \\ generative adversarial network & GAN \\ group convolution & GC \\ true positives & TPs \\ true negatives & TNs \\ false positives & FPs \\ false negatives & FNs \\ post-training quantization & PTQ \\ graphics processing unit & GPU \\ Native Development Kit & NDK \\ Java Native Interface & JNI \\ mean opinion score & MOS \\ \hline \hline \end{tabular}
\end{table}
Table 1: A table of acronyms.  
Figure 1: Significance and prospects of image aesthetic processing.  
evaluation of the system. Finally, Section 7 summarizes the study and discusses prospects for future research.