The various transformation techniques play vital role in the field of Digital Image Processing. In this paper, we propose FPGA implementation of optimized Karhunen–Loeve transform for image processing applications. The Data Format Conversion block is introduced to represent the input data to suitable format and are fed to the Covariance computation block to calculate corresponding covariance values with accuracy. The Optimized Square Root block has been designed in the Eigenvalue computation block to obtain eigenvalues which are in turn fed to the Eigenvector computation block to produce eigenvectors using Modified divider. Further the Karhunen–Loeve Transformed matrix of the input data is obtained by performing multiplication of eigenvectors with covariance values in the matrix multiplication block. The errors are introduced due to fixed point binary calculations and are minimized by novel Error correction block. The proposed architecture is tested on Sparan-6 (XC6SLX45-3CSG324) FPGA board. The performance of the architecture is compared with respect to hardware utilization and accuracy of various existing techniques to prove the efficiency.

中文翻译：

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针对图像处理应用的优化Karhunen-Loeve变换的FPGA实现

各种转换技术在数字图像处理领域中起着至关重要的作用。在本文中，我们提出了针对图像处理应用的优化Karhunen-Loeve变换的FPGA实现。引入数据格式转换块以将输入数据表示为合适的格式，并将其馈送到协方差计算块，以准确地计算相应的协方差值。已在特征值计算模块中设计了优化平方根模块，以获得特征值，然后将特征值馈送到特征向量计算模块，以使用修改的除法器生成特征向量。此外，通过在矩阵乘法块中执行特征向量与协方差值的乘法运算，可以获得输入数据的Karhunen-Loeve变换矩阵。由于定点二进制计算而引入了误差，并通过新颖的误差校正模块将其最小化。该架构在Sparan-6（XC6SLX45-3CSG324）FPGA板上进行了测试。比较了体系结构的性能，硬件利用率和各种现有技术的准确性，以证明效率。