Estimating the compression quality of an image by analysing blocking artefacts

Abstract: Determining the compression quality of an image is important for photo forensics and image enhancement algorithms. Unfortunately, there are a number of issues involved in determining the compression quality of an image from its metadata or quantization tables. A compression quality estimation algorithm based on visual inspection of detected compression artefacts is presented. This method detects and extracts feature samples around compression block corners. These feature samples are then pre-filtered to enhance the discontinuities produced by compression artefacts. The feature samples are then classified using a constricted Neural Network. The local quality estimations are then combined using robust statistics to estimate the maximum likelihood compression quality. This method was shown to accurately estimate the compression quality of an image without prior knowledge of the original uncompressed image

Text segmentation and recognition in unconstrained imagery

Abstract: In this paper, we present a novel method for recognizing and segmenting symbols and text in complex image sequences. The algorithm is designed to take advantage of the massive computing capability of parallel processing architectures..

A new adaptive colorization filter for video decompression

HD content is more in demand and requires a lot of bandwidth. In this paper, a new real-time adaptive colorization filter for HD videos is presented. This approach reduces the required bandwidth by reducing non-key frames in the HD video sequence to grayscale and colourizing these frames at the decompression stage. Additionally this technique determines the frame status based on the image information

Alignment invariant image comparison implemented on the GPU

Abstract: This paper proposes a GPU implemented algorithm to determine the differences between two binary images using Distance Transformations. These differences are invariant to slight rotation and offsets, making the technique ideal for comparisons between images that are not perfectly aligned..

Super features: a probabilistic approach to feature matching and correction

D.Phil. (Electrical and Electronic Engineering)Abstract: Please refer to full text to view abstract

Gaussian blur identification using scale-space theory

Image deblurring algorithms generally assume that the nature of the blurring function that degraded an image is known before an image can be deblurred. In the case of most naturally captured images the strength of the blur present in the image is not known. This paper proposes a method to identify the standard deviation of a Gaussian blur that has been applied to a single image with no a priori information about the conditions under which the image was captured. This simple method makes use of a property of the Gaussian function and the Gaussian scale space representation of an image to identify the amount of blur. This is in contrast to the majority of statistical techniques that require extensive training or complex statistical models of the blur for identification