Sch. of Math. Sci., Tel Aviv Univ.
IEEE Trans Image Process. 1996;5(1):4-15. doi: 10.1109/83.481666.
Schemes for image compression of black-and-white images based on the wavelet transform are presented. The multiresolution nature of the discrete wavelet transform is proven as a powerful tool to represent images decomposed along the vertical and horizontal directions using the pyramidal multiresolution scheme. The wavelet transform decomposes the image into a set of subimages called shapes with different resolutions corresponding to different frequency bands. Hence, different allocations are tested, assuming that details at high resolution and diagonal directions are less visible to the human eye. The resultant coefficients are vector quantized (VQ) using the LGB algorithm. By using an error correction method that approximates the reconstructed coefficients quantization error, we minimize distortion for a given compression rate at low computational cost. Several compression techniques are tested. In the first experiment, several 512x512 images are trained together and common table codes created. Using these tables, the training sequence black-and-white images achieve a compression ratio of 60-65 and a PSNR of 30-33. To investigate the compression on images not part of the training set, many 480x480 images of uncalibrated faces are trained together and yield global tables code. Images of faces outside the training set are compressed and reconstructed using the resulting tables. The compression ratio is 40; PSNRs are 30-36. Images from the training set have similar compression values and quality. Finally, another compression method based on the end vector bit allocation is examined.
提出了基于小波变换的黑白图像图像压缩方案。离散小波变换的多分辨率性质被证明是一种强大的工具，可以使用金字塔多分辨率方案沿垂直和水平方向分解图像。小波变换将图像分解为一组子图像，称为具有不同分辨率的形状，对应于不同的频带。因此，测试了不同的分配，假设高分辨率和对角线方向的细节对人眼的可见度较低。使用 LGB 算法对生成的系数进行矢量量化 (VQ)。通过使用一种近似重建系数量化误差的纠错方法，我们可以在低计算成本下，针对给定的压缩率最小化失真。测试了几种压缩技术。在第一个实验中，一起训练了几个 512x512 图像，并创建了公共表码。使用这些表，训练序列的黑白图像可以实现 60-65 的压缩比和 30-33 的 PSNR。为了研究不在训练集内的图像的压缩，一起训练了许多未经校准的人脸的 480x480 图像，并生成了全局表码。使用生成的表对训练集之外的人脸图像进行压缩和重构。压缩比为 40；PSNRs 为 30-36。来自训练集的图像具有相似的压缩值和质量。最后，研究了另一种基于末端向量位分配的压缩方法。
