In image processing, classification and compression are very common operations. Compression and classification algorithms are conventionally independent of each other and performed sequentially. However, some class distinctions may be lost after a minimum distortion compression. In this paper, two new schemes are developed that combine the compression and classification operations in order to optimize some classification metrics. In other words, the compression systems are improved under classification constraints. In the first scheme, compression is achieved by using Adaptive Differential Pulse Code Modulation (ADPCM). Optimization of filter coefficients is done by using a simple genetic algorithm (GA). In the second scheme, compression is achieved by image transform and quantization. The parameters in transform and quantization are adapted to improve the compression system and reduce the classification errors. Computer simulations are performed on hyperspectral images. The results are promising and illustrate the performance of the algorithms under various classification constraints and compression schemes.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Novel Algorithms for Optimal Compression Using Classification Metrics


    Contributors:
    Xie, Bei (author) / Bose, Tamal (author) / Merenyi, Erzsebet (author)


    Publication date :

    2008-03-01


    Size :

    4572225 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Image data compression metrics

    Beser, Nicholas | AIAA | 1993


    Image Data Compression Metrics

    Beser, N. / AIAA | British Library Conference Proceedings | 1993


    Using visual metrics to analyze lossy compression of noisy images

    Богдан Віталійович Коваленко / Володимир Васильович Лукін | DOAJ | 2021

    Free access