Image Compression Effects in Face Recognition Systems

With the growing number of face recognition applications in everyday life, image- an

quality of electrophoresis and isoelectric focusing images compressed using jpeg and jpeg2000

Aim: High quality of all image elements must be maintained for the purpose of telepathology. The aim of this study was to compress the images of electrophoresis and isoelectric focusing samples using JPEG (Joint Photographic Expert Group) and JPEG2000 algorithms, and to assess the quality of the compressed images before and after electronic transmission. Methods: Scanograms of serum protein electrophoresis samples of a patient with Zagreb albumin and albumin samples for isoelectric focusing of a patient with Krapina albumin were selected for the study together with a photographed scanogram of isoelectric focusing. Each image was compressed at eight compression rates (from 3.00 bpp (bit per pixel) to 0.1 bpp) using JPEG and JPEG2000 compression algorithms. All images (N = 51), both compressed and uncompressed, we retransmitted by email for assessment to eight medical biochemists: six from Croatia, one from Italy and one from Denmark. Image quality was also assessed by objective measures, i.e. compared to the quality of PSNR (Peak-Signal-to-Noise-Ratio), SNR (Signal-to-Noise-Ratio), OQF (Optimized Quality Factor) and MSE (Mean Squared Error) images. Results: All images compressed using the JPEG2000 algorithm were subjectively rated as excellent. Contrarily, images compressed using JPEG at 0.1 bpp were rated as completely useless, those at 0.2 bpp as moderately blurred, and those at 0.3-3.00 as excellent. At JPEG compression at 0.3 bpp, PSNR and SNR values corresponded to PSNR and SNR values obtained by JPEG2000 compression at 0.1 bpp

Combining cellular automata and local binary patterns for copy-move forgery detection

Detection of duplicated regions in digital images has been a highly investigated field in recent years since the editing of digital images has been notably simplified by the development of advanced image processing tools. In this paper, we present a new method that combines Cellular Automata (CA) and Local Binary Patterns (LBP) to extract feature vectors for the purpose of detection of duplicated regions. The combination of CA and LBP allows a simple and reduced description of texture in the form of CA rules that represents local changes in pixel luminance values. The importance of CA lies in the fact that a very simple set of rules can be used to describe complex textures, while LBP, applied locally, allows efficient binary representation. CA rules are formed on a circular neighborhood, resulting in insensitivity to rotation of duplicated regions. Additionally, a new search method is applied to select the nearest neighbors and determine duplicated blocks. In comparison with similar methods, the proposed method showed good performance in the case of plain/multiple copy-move forgeries and rotation/scaling of duplicated regions, as well as robustness to post-processing methods such as blurring, addition of noise and JPEG compression. An important advantage of the proposed method is its low computational complexity and simplicity of its feature vector representation