As smartphone cameras became more prevalent than traditional camera systems, the demand for precise measurements increased. This Ph.D. dissertation proposes using deep learning systems to evaluate image-quality criteria specific to smartphone camera evaluation, and more specifically texture evaluation. This dissertation addresses several limitations in current image-quality assessment methods for smartphone cameras. Deep learning systems struggle with computational complexity due to high-resolution smartphone images, and downsizing would lead to information loss for evaluating noise and details preservation. Consequently, it is essential to find the relevant image regions to assess a camera attribute to alleviate these problems. Additionally, the lack of suitable datasets hinders the development of learning-based methods aimed at benchmarking smartphone cameras.Furthermore, when comparing cameras, it is essential to capture the same content to facilitate direct comparison. In standard camera benchmarking protocols, multiple shots are collected from the same content. This setting deviates from traditional machine learning approaches, where training and test data are assumed to be independent and identically distributed (iid). However, the non-independent nature of our data is frequently overlooked in the image-quality assessment literature.To overcome these challenges, this research introduces several contributions: (i). A region selection method is introduced to automatically detect relevant regions for evaluating specific quality attributes. Adapting the class activation map method for a regression problem, we outperform traditional chart-based approaches in evaluating texture quality and permitting the usage of deep learning methods on charts shot in laboratory conditions. In this work, we use texture quality as an illustrative example of camera quality attributes. However, our methodology is designed to be applicable to other attributes, such as noise, as well. (ii) A new in-the-wild dataset is created to accurately reflect the complex mixture of defects commonly found in smartphone camera images and reflect the scenario of camera benchmarking, where several different scenes are shot by multiple camera devices. This dataset, annotated through pairwise comparisons, allows us to perform a large evaluation of different methods in different practical scenarios, setting guidelines for the usage of deep learning systems for camera quality evaluation. (iii) We introduce a new image quality assessment setup and method where we go beyond the traditional iid assumption. We consider multiple images with varying quality of the same content available at test time. We use the specificity of this camera quality estimation setting to enhance the quality prediction accuracy by introducing a batch-based pseudo-reference which allows us to use full-reference methods in the no-reference setting.