基于分形的小波分析压缩编码

分析了小波及分形在图形压缩编码中的特点 ,构造了Haar小波树 ,提出基于分形的小波域图像压缩编码的算法。实验证明 :该方法在相近的压缩比的情况下 ,使重建图像的PSNR增加 4 1,图像压缩速度明显提高 ,并且重建图像的主观视觉质量也优于JPEG。     

一种彩色图像的小波域预测编码方法

彩色图像存在空间相关性和帧间相关性,如何有效的去除这两种相关性是提高彩色图像压缩比的关键。提出一种小波变换域的自适应预测无损压缩方法,分为三个步骤:首先对彩色图像各个分量进行可逆整数小波变换减小空间相关性,然后利用联想记忆神经网络对小波系数图像分别进行空间预测和帧间预测,进一步去除空间和帧间相关性,最后对预测残差进行算术编码。实验证明该算法是可行的和有效的,对测试图像的压缩效果优于JPEG LS标准。     

利用颜色编码辅助计算绝对相位的傅里叶轮廓术

针对传统去卷积算法时间需求的弊端,提出一种新的使用颜色编码辅助的绝对相位并行计算方法。该算法采用对光栅数目需求最少的傅里叶变换轮廓术（FTP）做为卷积相位求取的方法;颜色编码光栅被用来标识轮廓的序数。直接使用FTP计算出的卷积相位以及从彩色光栅中获得的轮廓序数,即可方便求出当前像素的绝对相位值;同时只用一副图像标识轮廓序数也比其他轮廓序数标识方法简单。本方法由于使用绝对相位计算方法,局部相位误差不会扩展。实验结果也证明了此算法对于多个分离物体以及复杂物体的有效性。     

A new perceptual difference model for diagnostically relevant quantitative image quality evaluation: A preliminary study

Purpose

Most objective image quality metrics average over a wide range of image degradations. However, human clinicians demonstrate bias toward different types of artifacts. Here, we aim to create a perceptual difference model based on Case-PDM that mimics the preference of human observers toward different artifacts.

Method

We measured artifact disturbance to observers and calibrated the novel perceptual difference model (PDM). To tune the new model, which we call Artifact-PDM, degradations were synthetically added to three healthy brain MR data sets. Four types of artifacts (noise, blur, aliasing or “oil painting” which shows up as flattened, over-smoothened regions) of standard compressed sensing (CS) reconstruction, within a reasonable range of artifact severity, as measured by both PDM and visual inspection, were considered. After the model parameters were tuned by each synthetic image, we used a functional measurement theory pair-comparison experiment to measure the disturbance of each artifact to human observers and determine the weights of each artifact's PDM score. To validate Artifact-PDM, human ratings obtained from a Double Stimulus Continuous Quality Scale experiment were compared to the model for noise, blur, aliasing, oil painting and overall qualities using a large set of CS-reconstructed MR images of varying quality. Finally, we used this new approach to compare CS to GRAPPA, a parallel MRI reconstruction algorithm.

Results

We found that, for the same Artifact-PDM score, the human observer found incoherent aliasing to be the most disturbing and noise the least. Artifact-PDM results were highly correlated to human observers in both experiments. Optimized CS reconstruction quality compared favorably to GRAPPA's for the same sampling ratio.

Conclusions

We conclude our novel metric can faithfully represent human observer artifact evaluation and can be useful in evaluating CS and GRAPPA reconstruction algorithms, especially in studying artifact trade-offs.