Peak Transform for Efficient Image Representation and Coding
Abstract
In this work, we introduce a nonlinear geometric transform, called peak transform (PT), for efficient image representation
and coding. The proposed PT is able to convert
high-frequency signals into low-frequency ones, making them much easier to be compressed. Coupled with wavelet transform
and subband decomposition, the PT is able to significantly reduce signal energy in high-frequency subbands and achieve a significant transform coding gain. This has important applications in efficient
data representation and compression. To maximize the transform coding gain, we develop a dynamic programming solution for
optimum PT design. Based on PT, we design an image encoder, called the PT encoder, for efficient image compression. Our extensive
experimental results demonstrate that, in wavelet-based subband decomposition, the signal energy in high-frequency subbands can be reduced by up to 60% if a PT is applied. The
PT image encoder outperforms state-of-the-art JPEG2000 and H.264 (INTRA) encoders by up to 2-3 dB in peak signal-to-noise ratio (PSNR), especially for images with a significant amount
of high-frequency components. Our experimental results also show that the proposed PT is able to efficiently capture and preserve high-frequency image features (e.g., edges) and yields significantly improved visual quality.
Citation
Zhihai He, “Peak transform for efficient image representation and coding”, IEEE Transactions on Image Processing, Vol. 16, No. 7, pp. 1741-1754, July. 2007.
Rights
OpenAccess.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License.