Modification of standard image compression methods for correlation-based pattern recognition

Abstract

Standard image compression algorithms may not perform well in compressing images for pattern recognition applications, since they aim at retaining image fidelity in terms of perceptual quality rather than preserving spectrally significant information for pattern recognition. New compression algorithms for pattern recognition are therefore investigated, which are based on the modification of the standard compression algorithms to simultaneously achieve higher compression ratio and improved pattern recognition performance. This is done by emphasizing middle and high frequencies and discarding low frequencies according to a new distortion measure for compression. The operations of denoising, edge enhancement, and compression can be integrated in the same encoding process in the proposed compression algorithms. Simulation results show the effectiveness of the proposed compression algorithms.

Impact of Image Compression on In Vitro Cell Migration Analysis

“…However, for applications in, e.g., pattern recognition (or any other automated image analysis or vision task), it is not obvious if compression algorithms exhibiting better subjective or objective IQM are also better in a particular pattern recognition context. Compression algorithms tuned for applications in the pattern recognition context have thus been proposed, which are, e.g., based on the modification of the standard JPEG compression algorithm emphasizing middle and high frequencies and discarding low frequencies (the standard JPEG quantization matrix is rotated by 180 degrees) [2].…”