PG2014short

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https://diglib.eg.org/handle/10.2312/7750

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Browsing PG2014short by Subject "Digitization and Image Capture"

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    Parallel BTF Compression with Multi-Level Vector Quantization in OpenCL
    (The Eurographics Association, 2014) Egert, Petr; Vlastimil, Havran; John Keyser and Young J. Kim and Peter Wonka
    Bidirectional Texture Function (BTF) as an effective visual fidelity representation of surface appearance is becoming more and more widely used. In this paper we report on contributions to BTF data compression for multi-level vector quantization. We describe novel decompositions that improve the compression ratio by 15% in comparison with the original method, without loss of visual quality. Further, we show how for offline storage the compression ratio can be increased by 33% in total by Huffman coding. We also show that efficient parallelization of the vector quantization algorithm in OpenCL can reduce the compression time by factor of 9 on a GPU. The results for the new compression algorithm are shown on six low dynamic range BTFs and four high dynamic range publicly available BTF samples. Our method allows for real time synthesis on a GPU.
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    Random Sparse Coded Aperture for Lensless Imaging
    (The Eurographics Association, 2014) Wang, Zhenglin; Lee, Ivan; John Keyser and Young J. Kim and Peter Wonka
    This paper develops a computational lensless imaging system based on a random sparse coded aperture. The camera consists of a thin mask with a coded pattern and a standard sensor array. The proposed coded aperture contains multiple square pinholes, and forms a superposition of multiple pinhole images. In order to reduce the artefact due to diffraction or interference and simultaneously to facilitate the fabrication, the pinholes are designed bigger than some other proposed ones, and sparsely spread on the mask. Only the diffraction pattern for one pinhole imaging model needs be taken into account to improve the angular resolution. An arising issue is that the resulting optical transfer function (OTF) involves many zero-value spectrums, which adversely affects the reconstruction quality with conventional image decoding techniques. We introduce a reselection scheme, which selects partial Fourier samples to reduce the impact of zero entries in OTF. Then, the total variation minimization with quadratic constraints algorithm is applied to attain a good quality reconstruction