HIGH-SPEED PIV ANALYSIS USING COMPRESSED IMAGE CORRELATION

With the development of Holographic PIV (HPIV) and PIV Cinematography (PIVC), the need for a computationally efficient algorithm capable of processing images at video rates has emerged. This paper presents one such algorithm, sparse array image correlation. This algorithm is base d on the sparse format of image data-a format well suited to the stora ge of highly segmented images. Ir utilizes art image compression schem e that retains pixel values in high intensity gradient areas eliminati ng low information background regions. The remaining pixels are stored in sparse format along with their relative locations encoded into 32 bit words. The result is a highly reduced image data set that retains the original correlation information of the image. Compression ratios of 30:1 using this method are typical. As a result, far fewer memory c alls and data entry comparisons are required to accurately determine t race particle movement. In addition, by utilizing art error correlatio n function, pixel comparisons are made through single integer calculat ions eliminating time consuming multiplication and floating point arit hmetic. Thus, this algorithm typically results in much higher correlat ion speeds and lower memory requirements than spectral and image shift ing correlation algorithms. This paper describes the methodology of sp arse array con elation as well as the speed, accuracy, and limitations of this unique algorithm. While the study presented here focuses on t he process of correlating images stored in sparse format, the details of an image compression algorithm based on intensity gradient threshol ding is presented and its effect on image correlation is discussed to elucidate the limitations and applicability of compression based PIV p rocessing.