L2 Optimized Predictive Image Coding with L∞ Bound

Abstract

<p>In many scientific, medical and defense applications of image/video compression, an <em>l</em>_∞error bound is required. However, pure <em>l</em>_∞-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 <em>l</em>_∞-based image coding methods suffer from poor rate control. In contrast, the <em>l</em>₂ error metric aims for average fidelity and hence preserves the subtlety of smooth waveforms better than the <em>l</em>_∞ error metric and it offers fine granularity in rate control; but pure <em>l</em>₂-based image coding methods (e.g., JPEG 2000) cannot bound individual errors as <em>l</em>_∞-based methods can. This thesis presents a new compression approach to retain the benefits and circumvent the pitfalls of the two error metrics.</p>