The aim of this paper is to examine a set of wavelet functions (wavelets) for implementation in a still image compression system and to highlight the benefit of this transform relating to today's methods. The paper discusses important features of wavelet transform in compression of still images, including the extent to which the quality of image is degraded by the process of wavelet compression and decompression. Image quality is measured objectively, using peak signal-to-noise ratio or picture quality scale, and subjectively, using perceived image quality. The effects of different wavelet functions, image contents and compression ratios are assessed. A comparison with a discrete-cosine-transform-based compression system is given. Our results provide a good reference for application developers to choose a good wavelet compression system for their application.

Colorization is a process of converting grayscale images into visually acceptable color images. The main goal is to convince the viewer of the authenticity of the result. Grayscale images that need to be colorized are, in most cases, images with natural scenes. Over the last 20 years a wide range of colorization methods has been developed – from algorithmically simple, yet time- and energy-consuming because of unavoidable human intervention to more complicated, but simultaneously more automated methods. Automatic conversion has become a challenging area that combines machine learning and deep learning with art. This paper presents an overview and evaluation of grayscale image colorization methods and techniques applied to natural images. The paper provides a classification of existing colorization methods, explains the principles on which they are based, and highlights their advantages and disadvantages. Special attention is paid to deep learning methods. Relevant methods are compared in terms of image quality and processing time. Different metrics for color image quality assessment are used. Measuring the perceived quality of a color image is challenging due to the complexity of the human visual system. Multiple metrics used to evaluate colorization methods provide results by determining the difference between the predicted color value and the ground truth, which in several cases is not in coherence with image plausibility. The results show that user-guided neural networks are the most promising category for colorization because they successfully combine human intervention and neural network automation.