一种基于Wyner-Ziv结构的贝尔模板图像编码方法

 该文提出一种基于Wyner-Ziv(WZ)结构的四通道贝尔模板图像分布式编解码方法。在编码端对贝尔模板图像进行结构分离转换，形成4个分量图像并分别执行离散余弦变换，依据拉格朗日代价函数的收敛性，利用Lloyd迭代算法设计了全局优化的量化器，采用Slepian-Wolf(SW)信道编码方法对各分量变换系数的量化输出进行独立编码，在解码端利用亮度分量作为边信息，联合解码重构贝尔模板图像。实验表明，在高速率情况下，其率失真性能得到较好的改善。

A Coding Method of Bayer Pattern Image Based on Wyner-Ziv Structure

Based on Wyner-Ziv (WZ) codec structure, a four-channel distributed codec methods for Bayer pattern image is proposed. First, Bayer pattern image is separated and converted into four component image, and each component image is transformed into frequency domain by Discreted Cosine Transform (DCT). According to the convergence of Lagrangian cost function, an universal optimal quantizier is designed by Lloyd iterative algorithm. The quantized DCT coefficients in each channel are encoded independently by employing Slepian-Wolf (SW) coder. At the side of decoder, the side information is generated from illumination component image and available for the SW decoder and the inverse quantizier. Finally, the Bayer pattern image is rebuilt at the side of decoder. The experiments show that the proposed method can improve rate-distortion performance of Bayer pattern image codec at high rate.