Image Enhancement Using Vector Quantisation Based Interpolation.

TL;DR: A novel method of image expansion using vector quantisation that is inspired by fractal coding and uses a statistical model of the relationship between details at different scales of the image to interpolate detail at one octave above the highest spatial frequency in the original image.

Abstract: We present a novel method of image expansion using vector quantisation. The algorithm is inspired by fractal coding and uses a statistical model of the relationship between details at different scales of the image to interpolate detail at one octave above the highest spatial frequency in the original image. Our method aims at overcoming the drawbacks associated with traditional approaches such as pixel interpolation, which smoothes the scaled-up images, or fractal coding, which bears high computational cost and has limited use due to patent restrictions. The proposed method is able to regenerate plausible image detail that was irretrievable when traditional approaches are used. The vector quantisation-based method outperforms conventional approaches in terms of both objective and subjective evaluations. 1 Introduction Digital cinema sequences can be captured at a number of different resolutions, for example 2K pixels across or 4K pixels across. The cameras used for high resolutions are expensive and the data files they produce are large. Because of this, studios may chose to capture some sequences at lower resolution and others at high resolution. The different resolution sequences are later merged during post production. The merger requires that some form of image expansion be performed on the lower resolution sequences. In this paper we present a new method of doing the image expansion that has some advantages over the orthodox interpolation methods. The paper is organised as follows. Section 2 will review some of the existing techniques of image expansion and highlight their shortcomings. In Section 3, we will describe the proposed algorithm in details including the process of training the algorithm, constructing the library used in it, and producing as well as enhancing the expanded image using the algorithm. Section 4 contains our experimental results in which our proposed method is evaluated. The paper concludes in Section 5.