Browsing by Author "Debattista, K."

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    Application‐Specific Tone Mapping Via Genetic Programming
    (© 2018 The Eurographics Association and John Wiley & Sons Ltd., 2018) Debattista, K.; Chen, Min and Benes, Bedrich
    High dynamic range (HDR) imagery permits the manipulation of real‐world data distinct from the limitations of the traditional, low dynamic range (LDR), content. The process of retargeting HDR content to traditional LDR imagery via tone mapping operators (TMOs) is useful for visualizing HDR content on traditional displays, supporting backwards‐compatible HDR compression and, more recently, is being frequently used for input into a wide variety of computer vision applications. This work presents the automatic generation of TMOs for specific applications via the evolutionary computing method of genetic programming (GP). A straightforward, generic GP method that generates TMOs for a given fitness function and HDR content is presented. Its efficacy is demonstrated in the context of three applications: Visualization of HDR content on LDR displays, feature mapping and compression. For these applications, results show good performance for the generated TMOs when compared to traditional methods. Furthermore, they demonstrate that the method is generalizable and could be used across various applications that require TMOs but for which dedicated successful TMOs have not yet been discovered. High dynamic range (HDR) imagery permits the manipulation of real‐world data distinct from the limitations of the traditional, low dynamic range (LDR), content. The process of retargeting HDR content to traditional LDR imagery via tone mapping operators (TMOs) is useful for visualizing HDR content on traditional displays, supporting backwards‐compatible HDR compression and, more recently, is being frequently used for input into a wide variety of computer vision applications. This work presents the automatic generation of TMOs for specific applications via the evolutionary computing method of genetic programming (GP).
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    Audiovisual Resource Allocation for Bimodal Virtual Environments
    (© 2018 The Eurographics Association and John Wiley & Sons Ltd., 2018) Doukakis, E.; Debattista, K.; Harvey, C.; Bashford‐Rogers, T.; Chalmers, A.; Chen, Min and Benes, Bedrich
    Fidelity is of key importance if virtual environments are to be used as authentic representations of real environments. However, simulating the multitude of senses that comprise the human sensory system is computationally challenging. With limited computational resources, it is essential to distribute these carefully in order to simulate the most ideal perceptual experience. This paper investigates this balance of resources across multiple scenarios where combined audiovisual stimulation is delivered to the user. A subjective experiment was undertaken where participants (N=35) allocated five fixed resource budgets across graphics and acoustic stimuli. In the experiment, increasing the quality of one of the stimuli decreased the quality of the other. Findings demonstrate that participants allocate more resources to graphics; however, as the computational budget is increased, an approximately balanced distribution of resources is preferred between graphics and acoustics. Based on the results, an audiovisual quality prediction model is proposed and successfully validated against previously untested budgets and an untested scenario.Fidelity is of key importance if virtual environments are to be used as authentic representations of real environments. However, simulating the multitude of senses that comprise the human sensory system is computationally challenging. With limited computational resources, it is essential to distribute these carefully in order to simulate the most ideal perceptual experience. This paper investigates this balance of resources across multiple scenarios where combined audiovisual stimulation is delivered to the user. A subjective experiment was undertaken where participants (N=35) allocated five fixed resource budgets across graphics and acoustic stimuli.
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    Frame Rate vs Resolution: A Subjective Evaluation of Spatiotemporal Perceived Quality Under Varying Computational Budgets
    (© 2018 The Eurographics Association and John Wiley & Sons Ltd., 2018) Debattista, K.; Bugeja, K.; Spina, S.; Bashford‐Rogers, T.; Hulusic, V.; Chen, Min and Benes, Bedrich
    Maximizing performance for rendered content requires making compromises on quality parameters depending on the computational resources available . Yet, it is currently unclear which parameters best maximize perceived quality. This work investigates perceived quality across computational budgets for the primary spatiotemporal parameters of resolution and frame rate. Three experiments are conducted. Experiment 1 (n = 26) shows that participants prefer fixed frame rates of 60 frames per second (fps) at lower resolutions over 30 fps at higher resolutions. Experiment 2 (n = 24) explores the relationship further with more budgets and quality settings and again finds 60 fps is generally preferred even when more resources are available. Experiment 3 (n = 25) permits the use of adaptive frame rates, and analyses the resource allocation across seven budgets. Results show that while participants allocate more resources to frame rate at lower budgets the situation reverses once higher budgets are available and a frame rate of around 40 fps is achieved. In the overall, the results demonstrate a complex relationship between frame rate and resolution's effects on perceived quality. This relationship can be harnessed, via the results and models presented, to obtain more cost‐effective virtual experiences.Maximizing performance for rendered content requires making compromises on quality parameters depending on the computational resources available. Yet, it is currently unclear which parameters best maximize perceived quality. This work investigates perceived quality across computational budgets for the primary spatiotemporal parameters of resolution and frame rate. Three experiments are conducted. Experiment 1 (n = 26) shows that participants prefer fixed frame rates of 60 frames per second (fps) at lower resolutions over 30 fps at higher resolutions. Experiment 2 (n = 24) explores the relationship further with more budgets and quality settings and again finds 60 fps is generally preferred even when more resources are available. Experiment 3 (n = 25) permits the use of adaptive frame rates, and analyses the resource allocation across seven budgets. Results show that while participants allocate more resources to frame rate at lower budgets the situation reverses once higher budgets are available and a frame rate of around 40 fps is achieved.