Abstract
This paper proposes an efficient compression scheme for compressing RGB color images based on feature extraction with the combination of DCT transform and the Peano-Hilbert Scan. The RGB color image is converted into YCbCr in order to extract the color and the texture features. The DCT transform is applied to the extracted luma and the chroma component to reduce the redundancy. Peano-Hilbert scanning is performed over the DCT matrix which increases the PSNR of the reconstructed image. The proposed bi-mode quantization is applied to preserve the image quality. The quantized coefficients are encoded using the lossless Huffman encoding. The efficiency of the proposed compression scheme has been implemented and compared with other existing compression techniques. The proposed compression method can achieve a higher fidelity and faster decompression time compared to other lossy compression methods. Thus, Compression based on feature extraction contributes to better performance.
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Ashpin Pabi, D.J., Aruna, P., Puviarasan, N. (2018). Color Image Compression Based on Feature Extraction. In: Abraham, A., Cherukuri, A., Madureira, A., Muda, A. (eds) Proceedings of the Eighth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2016). SoCPaR 2016. Advances in Intelligent Systems and Computing, vol 614. Springer, Cham. https://doi.org/10.1007/978-3-319-60618-7_37
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DOI: https://doi.org/10.1007/978-3-319-60618-7_37
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