Grayscale and colour image Codec based on matching pursuit in the spatio-frequency domain

This report presents and evaluates a novel idea for scalable lossy colour image coding with Matching Pursuit (MP) performed in a transform domain. The benefits of the idea of MP performed in the transform domain are analysed in detail. The main contribution of this work is extending MP with wavelets to colour coding and proposing a coding method. We exploit correlations between image subbands after wavelet transformation in RGB colour space. Then, a new and simple quantisation and coding scheme of colour MP decomposition based on Run Length Encoding (RLE), inspired by the idea of coding indexes in relational databases, is applied. As a final coding step arithmetic coding is used assuming uniform distributions of MP atom parameters. The target application is compression at low and medium bit-rates. Coding performance is compared to JPEG 2000 showing the potential to outperform the latter with more sophisticated than uniform data models for arithmetic coder. The results are presented for grayscale and colour coding of 12 standard test images.

Colour image coding with matching pursuit in the spatio-frequency domain

We present and evaluate a novel idea for scalable lossy colour image coding with Matching Pursuit (MP) performed in a transform domain. The idea is to exploit correlations in RGB colour space between image subbands after wavelet transformation rather than in the spatial domain. We propose a simple quantisation and coding scheme of colour MP decomposition based on Run Length Encoding (RLE) which can achieve comparable performance to JPEG 2000 even though the latter utilises careful data modelling at the coding stage. Thus, the obtained image representation has the potential to outperform JPEG 2000 with a more sophisticated coding algorithm.

Contrast matching and discrimation in human vision

The aim of this work was to investigate human contrast perception at various contrast levels ranging from detection threshold to suprathreshold levels by using psychophysical techniques. The work consists of two major parts. The first part deals with contrast matching, and the second part deals with contrast discrimination. Contrast matching technique was used to determine when the perceived contrasts of different stimuli were equal. The effects of spatial frequency, stimulus area, image complexity and chromatic contrast on contrast detection thresholds and matches were studied. These factors influenced detection thresholds and perceived contrast at low contrast levels. However, at suprathreshold contrast levels perceived contrast became directly proportional to the physical contrast of the stimulus and almost independent of factors affecting detection thresholds. Contrast discrimination was studied by measuring contrast increment thresholds which indicate the smallest detectable contrast difference. The effects of stimulus area, external spatial image noise and retinal illuminance were studied. The above factors affected contrast detection thresholds and increment thresholds measured at low contrast levels. At high contrast levels, contrast increment thresholds became very similar so that the effect of these factors decreased. Human contrast perception was modelled by regarding the visual system as a simple image processing system. A visual signal is first low-pass filtered by the ocular optics. This is followed by spatial high-pass filtering by the neural visual pathways, and addition of internal neural noise. Detection is mediated by a local matched filter which is a weighted replica of the stimulus whose sampling efficiency decreases with increasing stimulus area and complexity. According to the model, the signals to be compared in a contrast matching task are first transferred through the early image processing stages mentioned above. Then they are filtered by a restoring transfer function which compensates for the low-level filtering and limited spatial integration at high contrast levels. Perceived contrasts of the stimuli are equal when the restored responses to the stimuli are equal. According to the model, the signals to be discriminated in a contrast discrimination task first go through the early image processing stages, after which signal dependent noise is added to the matched filter responses. The decision made by the human brain is based on the comparison between the responses of the matched filters to the stimuli, and the accuracy of the decision is limited by pre- and post-filter noises. The model for human contrast perception could accurately describe the results of contrast matching and discrimination in various conditions.