It has been investigated that at low bit rates, downsampling prior to coding and upsampling after decoding can achieve better compression performance than standard coding algorithms, e.g., JPEG and H. 264/AVC. However, at high bit rates, the sampling-based schemes generate more distortion. Additionally, the maximum bit rate for the sampling-based scheme to outperform the standard algorithm is image-dependent. In this paper, a practical adaptive image coding algorithm based on the cubic-spline interpolation (CSI) is proposed. This proposed algorithm adaptively selects the image coding method from CSI-based modified JPEG and standard JPEG under a given target bit rate utilizing the so called ρ-domain analysis. The experimental results indicate that compared with the standard JPEG, the proposed algorithm can show better performance at low bit rates and maintain the same performance at high bit rates.
Cubic-spline interpolation (CSI) scheme is known to be designed to resample the discrete image data based on the leastsquares
method with the cubic convolution interpolation (CCI) function. It is superior in performance to other
interpolation functions for digital image processing. In this paper, an improved CSI scheme that combines the leastsquares
method with an eight-point cubic interpolation kernel is developed in order to improve the performance of the
original CSI scheme. Either the FFT/Winograd DFT or the fast direct computation algorithm can also be used to perform
the circular convolution needed in this improved CSI scheme. Furthermore, its correlated image data and auto-correlated
filter coefficients are also accurately calculated in this paper. Experimental results indicate that the proposed improved
CSI scheme yields a much better quality of reconstructed image than existing interpolation algorithms.