# Towards Automatic Image Enhancement with Genetic Programming and Machine Learning
## 4 Experiments in Real Estate Image Marketing
### Image Quality Assessment  
Although our work has its focus on IE, it is essential to understand how an image's quality can be measured. [PERSON] et al. proposes the definition of image quality in three levels: fidelity, perception, and aesthetics [34]. Fidelity is how well the image preserved the original information. Perception is how well the image is perceived according to every part of the HVS. Lastly, the image's aesthetic is the most subjective level because it varies from person to person. It is also the most difficult to measure objectively because _\"aesthetics is too nonrepresentational to be characterized using mathematical models\"_[34]. Since we are dealing with image quality measurements, which most often is derived from a subjective appreciation, we must have a deterministic and automated way of qualifying an image's quality. To tackle this problem, we made use of 4 distinct _no-reference_ IQA tools, where _no-reference_ means that the evaluation does not depend on a target nor reference image to evaluate the quality of an input image.  
_PhotoLike_ (_PHIL_) is an IQA service provided by the Spanish company with the same name. This service is based on a proprietary AI system [33] that predicts the commercial attractiveness of each image, based on human evaluations. More than a million human evaluations of real estate images were employed to build a dataset for this AI. At the time of this experiment, only authentic real estate images, obtained from real estate portals, were employed in this dataset. This service receives an image and returns a value from 1 to 10, where 1 means the worst attractiveness, and 10 the best attractiveness.  
Blind Image Spatial Quality Evaluator [35] (_BRISQUIE_) is a _no-reference_ IQA tool, proposed by [PERSON] et al., used in image enhancement contexts [36; 37]. As opposed to the previous methods, _BRISQUIE_ is based on a set of Classical feature extraction procedures that computes a collection of 36 features per image. This tool originally outputs a value between 0 and 100, where 0 represents the best quality, and 100 the worst. However, for the outputs to be in concordance with the previously presented methods, the output was mapped to a 1 to 10 range, where 1 means is the worst score, and 10 the best.  
NIMA [38] is a _no-reference_ IQA tool based on a deep CNN, proposed by [PERSON] and [PERSON]. The paper highlights how the same architecture, trained with different datasets, leads to state-of-the-art performance predicting technical and aesthetic scores. As the paper states, NIMA technical judgment considers noise, blur, and compression artefacts, among other image features. On the other hand, the NIMA aesthetic evaluation aims to quantify the semantic level characteristics associated with images' emotions and beauty. Both provided models predict the final score as an average of a distribution of scores between 1 and 10, where 1 means the worst score, and 10 the best. Both models, use photographs with high user rating from the _dpchallenge_ ([[https://www.dpchallenge.com/](https://www.dpchallenge.com/)]([https://www.dpchallenge.com/](https://www.dpchallenge.com/)), accessed on 28 September 2021) website.