ℓ2 OPTIMIZED PREDICTIVE IMAGE CODING WITH ℓ∞ BOUND

In many scientific, medical and defense applications of image/video compression, an $\ell_{\infty}$ error bound is required. However, pure $\ell_{\infty}$-optimized image coding, colloquially known as near-lossless image coding, is prone to structured errors such as contours and speckles if the bit rate is not sufficiently high; moreover, previous $\ell_{\infty}$-based image coding methods suffer from poor rate control. In contrast, the $\ell_{2}$ error metric aims for average fidelity and hence preserves the subtlety of smooth waveforms better than the $\ell_{\infty}$ error metric and it offers fine granularity in rate control; but pure $\ell_{2}$-based image coding methods (e.g., JPEG 2000) cannot bound individual errors as the $\ell_{\infty}$-based methods can. This paper presents a new compression approach to retain the benefits and circumvent the pitfalls of the two error metrics.