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Author manuscript, published in SPARS'0 — Signal Processing with Adaptive Sparse Structured Representations (2009) Point Coding: Sparse Image Representation with Adaptive Shiftable-Kernel Dictionaries
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Point coding sparse image is a method used in image processing and computer vision to compress and represent images with fewer data points, reducing storage requirements and computational complexity.
Point coding sparse image is typically used and filed by researchers, developers, and professionals working in the fields of image processing, computer vision, and data compression.
To fill out a point coding sparse image, one needs to use specific algorithms and programming techniques to compress, encode, and reconstruct the image data using sparse representation. Various software libraries and tools are available for this purpose.
The purpose of point coding sparse image is to reduce the amount of data required to represent and store images without significant loss in image quality or visual fidelity. It aims to optimize storage and processing efficiency in image-related applications.
The information reported on a point coding sparse image typically includes the compressed image data, encoding parameters, compression ratio, reconstruction errors, and any additional metadata necessary for decompression and visualization.
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