The primary goal of computer graphics is to create images by rendering a scene under two constraints: quality, producing the image with as few artifacts as possible, and time, producing the image as fast as possible. Technology advances have both helped to satisfy these constraints, with Graphics Processing Unit (GPU) advances reducing image rendering times, and to exacerbate these constraints, with new HD and virtual reality displays increasing rendering resolutions. To meet both constraints, rendering uses texture mapping which maps 2D textures onto scene objects. Over time, the count and resolution of textures has increased, resulting in dramatic growth of data storage requirements. Compression can help to reduce these storage requirements.
I present a rigorous texture compression evaluation methodology using final rendered images. My method can account for masking effects introduced by the texture mapping process while leveraging the perceptual-rigor of current Image Quality Assessment metrics. Building on this evaluation methodology, I present a demonstration of guided texture compression optimization that minimizes the bitrate of compressed textures while maximizing the quality of final rendered images. Guided texture compression will help with the scalability problem for optimizing texture compression in real-world scenarios.
|Advisor:||Olano, Thomas M.|
|Commitee:||Adali, Tulay, Neerchal, Nagaraj, Oates, Tim, Rheingans, Penny|
|School:||University of Maryland, Baltimore County|
|School Location:||United States -- Maryland|
|Source:||DAI-B 78/02(E), Dissertation Abstracts International|
|Keywords:||Assessment, Compression, Quality, Texture|
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