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BigGuo--大郭's Projects

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End to End 3D Point Cloud Compression using Deep Learning

novel_extention_to_spiht_and_speck icon novel_extention_to_spiht_and_speck

Compression is useful because it helps to reduce the consumption of expensive resources, such as hard disk space or transmission bandwidth(computing). In order to achieve maximal storage and transmission capabilities, different compression algorithms should be compared in order to find an optimal technique for image compression. DWT(discrete wavelet transform) under goes two types of coding schemes i.e. tree based coding like SPIHT, EZW and block based coding like SPECK, EBCOT etc. for image compression. In this research, we studied the performance of SPIHT(set partitioning in Hierarchical Trees) and SPECK(set partitioning in embedded block), then we tried a novel extension to these popular schemes. We report results from our comparative study of different lossy image coding using gray scale and color image; PSNR(Peak Signal to Noise Ratio) and SSIM(Structural similarity) as traditional objective picture quality measures, the Algorithm Complexity in Terms of encoding/decoding Time and Memory Requirement. The objective of this work is to provide a quantitative and qualitative comparison of lossy image coding focusing on different bit rate.

pcc_geo_cnn icon pcc_geo_cnn

Learning Convolutional Transforms for Point Cloud Geometry Compression

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