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Image Quality Assessment of Computer-generated Images

Part of the SpringerBriefs in Computer Science Book Series

DOI: 10.1007/978-3-319-73543-6

ISBN: 978-3-319-73542-9

Publisher: Springer International Publishing - Springer Journals

Mar 10, 2018

Available

Introduction

Bigand, André; Dehos, Julien; Renaud, Christophe; Constantin, Joseph

Pages: 1 –5

[Image Quality Assessment (IQA) aims to characterize the visual quality of an image. Indeed, there are many sources of image degradation, for example, optical distortion, sensor noise, compression algorithms, etc., so IQA is useful to evaluate the perceived quality of an image or to optimize an...
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Monte Carlo Methods for Image Synthesis

Bigand, André; Dehos, Julien; Renaud, Christophe; Constantin, Joseph

Pages: 7 –17

[Image synthesis (also called rendering) consists in generating an image from a virtual 3D scene (composed of light sources, objects, materials, and a camera). Numerous rendering algorithms have been proposed since the 1970s: z-buffer (Catmull, A subdivision algorithm for computer display of...
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Visual Impact of Rendering on Image Quality

Bigand, André; Dehos, Julien; Renaud, Christophe; Constantin, Joseph

Pages: 19 –27

[To generate photo-realistic images, modern renderers generally use stochastic algorithms such as the path-tracing algorithm. These algorithms can produce high-quality images but may require a long computation time. Therefore, rendering is generally stopped after a given amount of time and the...
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Full-Reference Methods and Machine Learning

Bigand, André; Dehos, Julien; Renaud, Christophe; Constantin, Joseph

Pages: 29 –47

[This chapter introduces the application of machine learning to Image Quality Assessment (IQA) in the case of computer-generated images. The classical learning machines, like SVMs, are quickly remained and RVMs are presented to deal with this particular IQA case (noise features learning). A...
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Reduced-Reference Methods

Bigand, André; Dehos, Julien; Renaud, Christophe; Constantin, Joseph

Pages: 49 –70

[Reduced-reference image quality assessment needs no prior knowledge of reference image but only a minimal knowledge about processed images. A new reduced-reference image quality measure, based on SVMs and RVMs, using a supervised learning framework and synthetic images is proposed in this...
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No-Reference Methods and Fuzzy Sets

Bigand, André; Dehos, Julien; Renaud, Christophe; Constantin, Joseph

Pages: 71 –85

[This model can then be used in any progressive stochastic global illumination method in order to estimate the noise level of different parts of any image. A comparative study of this model with a simple test image demonstrates the good consistency between an added noise value and the results...
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General Conclusion and Perspectives

Bigand, André; Dehos, Julien; Renaud, Christophe; Constantin, Joseph

Pages: 87 –88

[High-quality computer-generated image generation techniques and the link between these techniques and perceptual noise are presented in the first two chapters (Chaps. 2 and 3). These chapters also present the high-quality images we can obtain with photo-realistic techniques on one hand and the...

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