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 `pyaesthetics` is a Python package for estimating visual features from still images. The package addresses the lack of available free, open-source, and easy-to-use tools for estimating a wide range of visual features. The API for `pyaesthetics` was designed to provide modules for various visual features commonly used in empirical aesthetics studies. It also offers simple entry points for conducting automated analysis for users with limited coding knowledge.
 
-Among the features, `pyaesthetics` allows for the estimation of brightness, contrast, saturation, visual complexity, symmetry, colorfulness, and color distribution. The updated list of features that can be estimated with `pyaesthetics` is available in the [repository of the project](https://github.com/Gabrock94/pyaesthetics) as well as in the [documentation of the project](https://prettywebsite.readthedocs.io/en/latest/index.html), together with installation instructions, a getting started guide, and a few examples of applications. `pyaesthetics` can be used to extract single or multiple features from images, and for features where visualization helps in the interpretation of the results (e.g., visual complexity by quadratic tree decomposition), plotting utilities are provided (e.g., Figure 1, Figure 2).
+Among the features, `pyaesthetics` allows for the estimation of brightness, contrast, saturation, visual complexity, symmetry, colorfulness, and color distribution. The updated list of features that can be estimated with `pyaesthetics` is available in the [repository of the project](https://github.com/Gabrock94/pyaesthetics) as well as in the [documentation of the project](https://prettywebsite.readthedocs.io/en/latest/index.html), together with installation instructions, a getting started guide, and a few examples of applications. `pyaesthetics` can be used to extract single or multiple features from images, and for features where visualization helps in the interpretation of the results (e.g., visual complexity by quadratic tree decomposition), plotting utilities are provided (e.g.: Quadratic Tree Decompositionm Figure 1).
 
 ![Sample of a figure generated by `pyaesthetics`'s plotting utilities. The image depicts the analysis of visual complexity, estimated via quadratic tree decomposition, of a still image from the AVI14 dataset [@miniukovich2014quantification]. Each square represents a leaf of the decomposition tree, with the total number of leaves providing a measure of visual complexity; a higher number of leaves indicates greater visual complexity. The visual representation helps in understanding which parts of the image contribute most to the overall visual complexity.](QTDsample.png){ width=100% }
 
-![Sample of three different color palettes of 5 different colors generated from a source figure (A). B represents a palette obtained using 16 named colors, C a palette using 140 named colors, while D represents a palette obtained with no named colors (colors clustering).](palettes.png){ width=100% }
+`pyaesthetics` is primarily aimed at researchers in the field of empirical aesthetics. However, its modules can also be useful for researchers in the Social Sciences, particularly Psychology, and Neuroscience, to explore the visual properties of stimuli used in various research projects (e.g. matching the brightness of visual images). Additionally, `pyaesthetics` may be employed by visual designers, artists, and other individuals who need to analyze the visual properties of images of different nature(e.g. color palette extraction, Figure 2). 
 
-`pyaesthetics` is primarily aimed at researchers in the field of empirical aesthetics. However, its modules can also be useful for researchers in the Social Sciences, particularly Psychology, and Neuroscience, to explore the visual properties of stimuli used in various research projects (e.g. matching the brightness of visual images). Additionally, `pyaesthetics` may be employed by visual designers, artists, and other individuals who need to analyze the visual properties of images of different nature(e.g. color palette extraction). 
+![Sample of three different color palettes of 5 different colors generated from a source figure (A). B represents a palette obtained using 16 named colors, C a palette using 140 named colors, while D represents a palette obtained with no named colors (colors clustering).](palettes.png){ width=100% }
 
 `pyaesthetics` is distributed under the GNU General Public License (Version 3), and the source code is available in a [public Git repository](https://github.com/Gabrock94/pyaesthetics), while the [documentation of the project](https://prettywebsite.readthedocs.io/en/latest/index.html) is served via Read the Docs.