LHE (logarithmical hopping encoding) is a computationally efficient image compression algorithm that exploits the Weber-Fechner law to encode the error between colour component predictions and the actual value of such components. More concretely, for each pixel, luminance and chrominance predictions are calculated as a function of the surrounding pixels and then the error between the predictions and the actual values are logarithmically quantised. The main advantage of LHE is that although it is capable of achieving a low-bit rate encoding with high quality results in terms of peak signal-to-noise ratio (PSNR) and image quality metrics with full-reference (FSIM) and non-reference (blind/referenceless image spatial quality evaluator), its time complexity is O(n) and its memory complexity is O(1). Furthermore, an enhanced version of the algorithm is proposed, where the output codes provided by the logarithmical quantiser are used in a pre-processing stage to estimate the perceptual relevance of the image blocks. This allows the algorithm to downsample the blocks with low perceptual relevance, thus improving the compression rate. The performance of LHE is especially remarkable when the bit per pixel rate is low, showing much better quality, in terms of PSNR and FSIM, than JPEG and slightly lower quality than JPEG-2000 but being more computationally efficient.

Data leakage is a problem that companies and organizations face every day around the world. Mainly the data leak caused by the internal threat posed by authorized personnel to manipulate confidential information. The main objective of this work is to survey the literature to detect the existing techniques to protect against data leakage and to identify the methods used to address the insider threat. For this, a literature review of scientific databases was carried out in the period from 2011 to 2022, which resulted in 42 relevant papers. It was obtained that from 2017 to date, 60% of the studies found are concentrated and that 90% come from conferences and publications in journals. Significant advances were detected in protection systems against data leakage with the incorporation of new techniques and technologies, such as machine learning, blockchain, and digital rights management policies. In 40% of the relevant studies, significant interest was shown in avoiding internal threats. The most used techniques in the analyzed DLP tools were encryption and machine learning.

This paper presents a new adaptive downsampling technique called elastic downsampling, which enables high compression rates while preserving the image quality. Adaptive downsampling techniques are based on the idea that image tiles can use different sampling rates depending on the amount of information conveyed by each block. However, current approaches suffer from blocking effects and artifacts that hinder the user experience. To bridge this gap, elastic downsampling relies on a Perceptual Relevance analysis that assigns sampling rates to the corners of blocks. The novel metric used for this analysis is based on the luminance fluctuations of an image region. This allows a gradual transition of the sampling rate within tiles, both horizontally and vertically. As a result, the block artifacts are removed and fine details are preserved. Experimental results (using the Kodak and USC Miscelanea image datasets) show a PSNR improvement of up to 15 dB and a superior SSIM (Structural Similarity) when compared with other techniques. More importantly, the algorithms involved are computationally cheap, so it is feasible to implement them in low-cost devices. The proposed technique has been successfully implemented using graphics processors (GPU) and low-power embedded systems (Raspberry Pi) as target platforms.

The Morse code, invented in 1838 for use in telegraphy, is one of the first examples of the practical use of data compression [1], where the most common letters of the alphabet are coded shorter than the rest of codes. From 1940 and after the development of the theory of information and the creation of the first computers, compression of information has been a constant and fundamental challenge among any type of researchers. The greater our understanding of the meaning of information, the greater our success at compressing.In the case of multimedia information, its nature allows lossy compression, reaching impossible compression rates compared with lossless algorithms. These "recent" lossy algorithms have been mainly based on information transformation to frequency domain and elimination of some of the information in that domain. Transforming the frequency domain has advantages but also involves inevitable computational costs.This thesis introduces a new multimedia compression algorithm called "LHE" (logarithmical Hopping Encoding) that does not require transformation to frequency domain, but works in the space domain. This feature makes LHE a linear algorithm of reduced computational complexity. The results of the algorithm are promising, outperforming the JPEG standard in quality and speed.The basis of the algorithm is the physiological response of the human eye to the light stimulus. The eye, like other senses, responds to the logarithm of the signal according with Weber law.The algorithm consists of several stages. One is the measurement of "perceptual relevance," a new metric that will allow us to measure the relevance of information in the subject's mind and based on it; degrade accordingly their contents, through what I have called "elastic downsampling". Elastic downsampling stage is an unprecedented new technique in digital image processing. It lets take more or less samples in different areas of an image based on their perceptual relevance. This thesis introduces the first steps for the development of what may become a new standard multimedia compression format (image, video and audio) free of patents and high performance in both speed and quality.

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