Comparison of Image Compression Methods Using Objective Measures towards Machine Recognition

In this paper, we provide objective measures to evaluate compression methods for machine recognition applications. Vidware Vision, a black box compression method developed by Vidware Incorporated, is used in the case and its performance is compared to existing compression methods, i.e., JPEG and JPEG 2000, based on various measures. The encoding and decoding time are used to characterize computational complexity. Fulland noreference image quality measures are exploited to describe distortions and degradations in the decompressed images. In addition, since this paper focuses on the performance of compression methods relating to machine recognition applications, we propose a non-separable rational function based Tenengrad (NSRT2) measure to evaluate the sharpness of decompressed images. Based on our experimental results, Vidware Vision TM is robust to changes in compression ratio and presents gradually degraded performance at a considerably slower speed in terms of computational complexity and image quality. Particularly, according to full-reference measures Vidware Vision outperforms JPEG and JPEG 2000 when the compression ratio is larger than 140. The effectiveness of our proposed NSRT2, as a new comparison tool, is also validated via experiments and performance comparisons with other tested measures. .

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