The measure of image (videos) quality remains a research challenge and a very active field of investigation considering image processing. One solution consists of providing a subjective score to the image quality (according to a reference or without reference) obtained from human observers. The setting of such psycho-visual tests is very expensive (considering time and human organization) and needs clear and strict proceedings. Algorithmic solutions have been developed (objective scores) to avoid such tests. Some of these techniques are based on the modeling of the Human Visual System (HVS) to mimic the human behavior, but they are complex. In the case of natural scenes, a great number of image (or video) quality databases exist that makes possible the validation of these different techniques. Soft computing (machine learning, fuzzy logic, etc.), widely used in many scientific fields such as biology, medicine, management sciences, financial sciences, plant control, etc., is also a very useful cross-disciplinary tool in image processing. These tools have been used to establish image quality and they are now wellknown.

This chapter covers the basics of TensorFlow, the deep learning framework. Deep learning does a wonderful job in pattern recognition, especially in the context of images, sound, speech, language, and time-series data. With the help of deep learning, you can classify, predict, cluster, and extract features. Fortunately, in November 2015, Google released TensorFlow, which has been used in most of Google’s products such as Google Search, spam detection, speech recognition, Google Assistant, Google Now, and Google Photos. Explaining the basic components of TensorFlow is the aim of this chapter.

This book is intended to be a first introduction to deep learning. Deep learning is a special kind of learning with deep artificial neural networks, although today deep learning and artificial neural networks are considered to be the same field. Artificial neural networks are a subfield of machine learning which is in turn a subfield of both statistics and artificial intelligence (AI). Artificial neural networks are vastly more popular in artificial intelligence than in statistics. Deep learning today is not happy with just addressing a subfield of a subfield, but tries to make a run for the whole AI. An increasing number of AI fields like reasoning and planning, which were once the bastions of logical AI (also called the Good Old-Fashioned AI, or GOFAI), are now being tackled successfully by deep learning. In this sense, one might say that deep learning is an approach in AI, and not just a subfield of a subfield of AI.

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