RIA: Vector Quantization for Image Compression and Processing

图像压缩是减少存储或传输图像所需的数据量的过程。传统上，它很少考虑可能在压缩步骤之前或之后的图像处理操作。建议研究图像压缩和图像处理可能合并为单一操作的领域，以及图像压缩可能导致改进或简化图像处理操作的领域。将开发用于压缩半色调二值图像的矢量量化算法，并将先压缩后半色调的图像与先半色调后压缩的图像进行比较。矢量量化器可以同时执行压缩和图像处理任务，如半色调、反傅立叶变换和收缩增强，也将被研究。如果压缩步骤的复杂性小于图像处理操作的复杂性，这将导致计算复杂性的降低。最后，我们将检验图像压缩是否会导致更简单或改进的图像处理操作，如图像分割、目标检测和医学图像中可疑区域的预筛选。

Image compression is the process of reducing the amount of data required to store or transmit an image. It has traditionally been done with little regard for image processing operations that may precede or follow the compression step. It is proposed to investigate areas in which image compression and image processing may be combined into a single operation, and also areas in which image compression may lead to improved or simpler image processing operations. Vector quantization algorithms for the compression of halftoned binary images will be developed and images will be compared that are first compressed and then halftoned with images that are halftoned and then compressed. Vector quantizers that can simultaneously perform both compression and image processing tasks such as halftoning, inverse Fourier transforming, and contract enhancement will also be investigated. This will lead to a reduction in computational complexity if the complexity of the compression step is less than that of the image processing operation. Finally, examinations will be done to see if image compression will lead to simpler or improved image processing operations such as image segmentation, target detection, and pre- screening for suspect regions in medical images.