Date of Award
Master of Applied Science (MASc)
Electrical and Computer Engineering
In many scientific, medical and defense applications of image/video compression, an l∞ error bound is required. However, pure l∞-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 l∞-based image coding methods suffer from poor rate control. In contrast, the l2 error metric aims for average fidelity and hence preserves the subtlety of smooth waveforms better than the l∞ error metric and it offers fine granularity in rate control; but pure l2-based image coding methods (e.g., JPEG 2000) cannot bound individual errors as l∞-based methods can. This thesis presents a new compression approach to retain the benefits and circumvent the pitfalls of the two error metrics.
Chuah, Sceuchin, "L2 Optimized Predictive Image Coding with L∞ Bound" (2013). Open Access Dissertations and Theses. Paper 7603.
McMaster University Library