You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: The Importance of Visual Attention in Improving the 3D-TV Viewing Experience: Overview and New Perspectives 
Chapter Title:  A. Background 
1. Abstract of Cited Paper (BIB001):  Polyester yarn having the desirable crimp properties of false-twist textured, heat-set yarn is produced from melt-spun and drawn bicomponent filaments wherein poly(ethylene terephthalate) of 12 to 15 relative viscosity is one component and a copolyester of 23 to 26 relative viscosity, preferably poly(ethylene/2,2-dimethyl-1,3-propylene terephthalate) containing about 90 mole percent ethylene terephthalate units, is the other component. The bicomponent filaments are annealed at constant length to crystallize the polyethylene terephthalate component, forwarded in a jetted stream of heated compressible fluid, impinged in a plasticized condition against a moving screen, cooled and then taken up from the screen. The process can be operated continuously at high speeds, e.g., windup speeds of 3,000 to 4,000 meters per minute. 
2. Abstract of Cited Paper (BIB002):  A visual attention system, inspired by the behavior and the neuronal architecture of the early primate visual system, is presented. Multiscale image features are combined into a single topographical saliency map. A dynamical neural network then selects attended locations in order of decreasing saliency. The system breaks down the complex problem of scene understanding by rapidly selecting, in a computationally efficient manner, conspicuous locations to be analyzed in detail. Index Terms—Visual attention, scene analysis, feature extraction, target detection, visual search. ———————— F ———————— 
3. Abstract of Cited Paper (BIB003):  The ability of human visual system to detect visual saliency is extraordinarily fast and reliable. However, computational modeling of this basic intelligent behavior still remains a challenge. This paper presents a simple method for the visual saliency detection. Our model is independent of features, categories, or other forms of prior knowledge of the objects. By analyzing the log-spectrum of an input image, we extract the spectral residual of an image in spectral domain, and propose a fast method to construct the corresponding saliency map in spatial domain. We test this model on both natural pictures and artificial images such as psychological patterns. The result indicate fast and robust saliency detection of our method. 
4. Abstract of Cited Paper (BIB004):  We propose a definition of saliency by considering what the visual system is trying to optimize when directing attention. The resulting model is a Bayesian framework from which bottom-up saliency emerges naturally as the self-information of visual features, and overall saliency (incorporating top-down information with bottom-up saliency) emerges as the pointwise mutual information between the features and the target when searching for a target. An implementation of our framework demonstrates that our model’s bottom-up saliency maps perform as well as or better than existing algorithms in predicting people’s fixations in free viewing. Unlike existing saliency measures, which depend on the statistics of the particular image being viewed, our measure of saliency is derived from natural image statistics, obtained in advance from a collection of natural images. For this reason, we call our model SUN (Saliency Using Natural statistics). A measure of saliency based on natural image statistics, rather than based on a single test image, provides a straightforward explanation for many search asymmetries observed in humans; the statistics of a single test image lead to predictions that are not consistent with these asymmetries. In our model, saliency is computed locally, which is consistent with the neuroanatomy of the early visual system and results in an efficient algorithm with few free parameters. 
5. Abstract of Cited Paper (BIB005):  Bartlett viewed thinking as a high level skill exhibiting ballistic properties that he called its “point of no return”. This paper explores one aspect of cognition through the use of a simple model task in which human subjects are asked to commit attention to a position in visual space other than fixation. This instruction is executed by orienting a covert (attentional) mechanism that seems sufficiently time locked to external events that its trajectory can be traced across the visual field in terms of momentary changes in the efficiency of detecting stimuli. A comparison of results obtained with alert monkeys, brain injured and normal human subjects shows the relationship of this covert system to saccadic eye movements and to various brain systems controlling perception and motion. In accordance with Bartlett's insight, the possibility is explored that similar principles apply to orienting of attention toward sensory input and orienting to the semantic structures used in thinking. 
6. Abstract of Cited Paper (BIB006):  The computational modeling of the visual attention is receiving increasing attention from the computer vision community. Several bottom-up models have been proposed. In spite of their complexities, these models are still a basic description of our visual system. Review of resulting approaches of these efforts are presented in the first part of this paper. Limitations of these approaches are introduced and several research trends are given. Among them, the most important one might be the use of prior knowledge, conjointly with the low-level visual features. Concomitantly with visual attention (VA) modeling progress, the image and video processing community is increasingly considering VA models in different fields or services. Current and future applications of VA models are discussed in the second part. 
7. Abstract of Cited Paper (BIB007):  The concept of surprise is central to sensory processing, adaptation, learning, and attention. Yet, no widely-accepted mathematical theory currently exists to quantitatively characterize surprise elicited by a stimulus or event, for observers that range from single neurons to complex natural or engineered systems. We describe a formal Bayesian definition of surprise that is the only consistent formulation under minimal axiomatic assumptions. Surprise quantifies how data affects a natural or artificial observer, by measuring the difference between posterior and prior beliefs of the observer. Using this framework we measure the extent to which humans direct their gaze towards surprising items while watching television and video games. We find that subjects are strongly attracted towards surprising locations, with 72% of all human gaze shifts directed towards locations more surprising than the average, a figure which rises to 84% when considering only gaze targets simultaneously selected by all subjects. The resulting theory of surprise is applicable across different spatio-temporal scales, modalities, and levels of abstraction.

Literature Review:
Research on visual attention modeling is nowadays at a crossroad between many different fields such as neuroscience, cognitive science, psychology, and image processing. Studies have indicated that viewers tend to focus their attention on specific areas of interest in the image and two mechanisms of visual attention have been identified: bottom-up and top-down  , BIB006 . Bottom-up attention relates to involuntary, automatic, and unconscious aspects of vision. It is mostly driven by signal characteristics. Top-down attention relates to voluntary and conscious aspects of vision. Eye-tracking experiments are conducted to study visual attention with two purposes: the recording of scan paths, usually represented or analyzed in terms of successions of fixations and saccades, and the identification of the locations of visual interest in the content (saliency). Models of visual attention are usually designed to produce (predict) saliency maps, which represent the location and level of visual interest of each area (or pixel) in the content. In this paper, we focus on the second aspect. The idea of modeling visual attention for saliency prediction based on the use of visual features and integration theory appeared BIB001 , and the idea of a biologically-plausible computational model followed BIB002 . Subsequently, models of visual attention have been proposed for various applications and have also been classified into three different categories: hierarchical models, statistical models and Bayesian models BIB007 . Most models proposed in the literature are based on a bottom-up architecture, often relying on the contrast detection of a number of low-level features such as luminance, color, orientation, motion, e.g., BIB003 - BIB004 , and may use the concept of rarity BIB005 or surprise BIB008 . Research on visual attention modeling and its applications has increasingly gained popularity. However, compared to the amount of works on still images, relatively few studies have investigated visual attention modeling on moving sequences. Furthermore, only a very small number of works related to visual attention on stereoscopic 3D content can currently be found in the literature. However, this field has recently attracted interest because of the emergence of 3D video (in cinema and home) and recent availability of high-definition 3D-capable equipment to capture and display stereoscopic content. Depending on the philosophy or architecture used for modeling visual attention, the extension of existing 2D visual attention models to 3D content is not straightforward, especially in a biologically plausible way. Finally, collecting 3D gaze patterns and 3D saliency maps using existing eye-tracking equipment raises serious challenges.