基于稀疏表示的低比特率可伸缩图像编码算法研究

根据图像的几何结构特性,从人类视觉系统特性出发,建立了Gabor感知多成份字典,进而模拟人类视觉通路的层次处理机制,构建了稀疏编码网络,能够有效去除图像中的高阶冗余,形成更为稀疏的表示。对稀疏表示系数重组后进行比特平面量化,实现了低比特率的可伸缩编码。实验结果表明,在低比特率下,本文算法压缩后重构图像的感知质量要明显优于JPEG2000,峰值信噪比也与其相当,并且对于图像中的边缘和纹理等细节保持效果更佳。

Low-Rate and Scalable Image Coding with Sparse Representations

Based on geometric properties of the local image structures and the perception characters of human visual system (HVS), a geometrically motivated Gabor multi-component perception dictionary is constructed. Moreover, inspired from the hierarchical information processing in the human visual path, a sparse coding network is proposed to reduce high order redundancy, thus provides much sparser representations of images. Furthermore, coefficients of repositioned atoms in sparse approximation are quantized by bit-plane quantization. It presents an effective low bit-rate and scalable image compression algorithm. Our simulation results show that, under the low bit-rate, our approach is comparable to the JPEG2000 in terms of PSNR value, while effectively preserves edges and textures structures and exhibits generally a better visual quality.