Nello Carrara Res. Inst. on Electromagn. Waves, CNR, Florence.
IEEE Trans Image Process. 1997;6(6):831-43. doi: 10.1109/83.585234.
In this paper, the effects of quantization noise feedback on the entropy of Laplacian pyramids are investigated. This technique makes it possible for the maximum absolute reconstruction error to be easily and strongly upper-bounded (near-lossless coding), and therefore, allows reversible compression. The entropy-minimizing optimum quantizer is obtained by modeling the first-order distributions of the differential signals as Laplacian densities, and by deriving a model for the equivalent memoryless entropy. A novel approach, based on an enhanced Laplacian pyramid, is proposed for the compression, either lossless or lossy, of gray-scale images. Major details are prioritized through a content-driven decision rule embedded in a uniform threshold quantizer with noise feedback. Lossless coding shows improvements over reversible Joint Photographers Expert Group (JPEG) and the reduced-difference pyramid schemes, while lossy coding outperforms JPEG, with a significant peak signal-to-noise ratio (PSNR) gain. Also, subjective quality is higher even at very low bit rates, due to the absence of the annoying impairments typical of JPEG. Moreover, image versions having resolution and SNR that are both progressively increasing are made available at the receiving end from the earliest retrieval stage on, as intermediate steps of the decoding procedure, without any additional cost.
本文研究了量化噪声反馈对拉普拉斯金字塔熵的影响。该技术使得最大绝对重建误差能够很容易且强地被上界限制（接近无损编码），从而允许进行可逆压缩。通过将差分信号的一阶分布建模为拉普拉斯密度，并推导出等效无记忆熵的模型，获得了熵最小化的最优量化器。提出了一种基于增强拉普拉斯金字塔的新方法，用于灰度图像的无损或有损压缩。主要细节通过嵌入在具有噪声反馈的均匀阈值量化器中的基于内容的决策规则进行优先级排序。无损编码显示出优于可逆联合图像专家组 (JPEG) 和减少差分金字塔方案的改进，而有损编码则具有显著的峰值信噪比 (PSNR) 增益，优于 JPEG。此外，由于没有 JPEG 典型的恼人干扰，即使在非常低的比特率下，主观质量也更高。此外，从最早的检索阶段开始，在接收端就可以提供具有分辨率和 SNR 都逐渐增加的图像版本，作为解码过程的中间步骤，无需任何额外成本。
