基于模糊混合预测的静态图像无损压缩

提出了一种基于自适应模糊混合预测的静态图像无损压缩方法．在预测准备阶段，根据一个预定的阈值计算出一组自适应线性预测器。用完全自组织简化自适应共振神经网络对得到的预测器进行训练；在预测图像过程中，训练预测器在模糊逻辑的意义上互相结合起来实现最终预测过程；在误差编码阶段，该方法使用了基于上下文的条件算术编码．实验表明，该方法的诸多特点使得它能在计算复杂性和预测效率间达到平衡，表现出优良的压缩性能．

Lossless Compression of Still Images Based on Fuzzy-Combination Prediction

An approach for lossless compression of still images based on fuzzy-combined adaptive prediction was proposed. In the phase of prediction preparation, a set of optimal autoregressive predictors is calculated adaptively based on a predetermined threshold. Then the predictors are trained by a neural network called FOSART. The predictors attained from procedure of training are incorporated in sense of fuzzy logic to implement the procedure of prediction. In the entropy coding phase, the context-based conditional adaptive arithmetic encoding is adopted in the proposed approach. The experiments demonstrate the characteristics make the approach achieve good tradeoff between computational complexity and efficiency of prediction and it has a good performance for lossless compression.