Karel Fliegel

Image compression techniques in the field of security technology: examples and discussion

The image compression techniques are widely applied in the field of security technology (perimeter supervision, CCTV in facilities, faces, fingerprints, car plates etc.). The extensive and dramatically expanding use of image information requires increasing capacity of either transmission channels or retrieving devices. The image compression techniques offer a partial solution of this problem but there are some shortcomings to be avoided. During last years, we have tested and evaluated a set of different image compression techniques from the security point of view in a relation to a standard visual quality evaluation. The results have been part-by-part presented at several previous Carnahan conferences (Lucas and Kanade, 2001; Swain and Ballard,1991; Comanaciu, 2003). The paper presents summarized information about these results and based upon them a comprehensive discussion and conclusions are derived. Moreover some new results related to the VQ (vector quantization) and video coding are presented too.

SIMToolbox: a MATLAB toolbox for structured illumination fluorescence microscopy.

UNLABELLED SIMToolbox is an open-source, modular set of functions for MATLAB equipped with a user-friendly graphical interface and designed for processing two-dimensional and three-dimensional data acquired by structured illumination microscopy (SIM). Both optical sectioning and super-resolution applications are supported. The software is also capable of maximum a posteriori probability image estimation (MAP-SIM), an alternative method for reconstruction of structured illumination images. MAP-SIM can potentially reduce reconstruction artifacts, which commonly occur due to refractive index mismatch within the sample and to imperfections in the illumination. AVAILABILITY AND IMPLEMENTATION SIMToolbox, example data and the online documentation are freely accessible at http://mmtg.fel.cvut.cz/SIMToolbox. CONTACT ghagen@uccs.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Performance evaluation of the emerging JPEG XT image compression standard

The upcoming JPEG XT is under development for High Dynamic Range (HDR) image compression. This standard encodes a Low Dynamic Range (LDR) version of the HDR image generated by a Tone-Mapping Operator (TMO) using the conventional JPEG coding as a base layer and encodes the extra HDR information in a residual layer. This paper studies the performance of the three profiles of JPEG XT (referred to as profiles A, B and C) using a test set of six HDR images. Four TMO techniques were used for the base layer image generation to assess the influence of the TMOs on the performance of JPEG XT profiles. Then, the HDR images were coded with different quality levels for the base layer and for the residual layer. The performance of each profile was evaluated using Signal to Noise Ratio (SNR), Feature SIMilarity Index (FSIM), Root Mean Square Error (RMSE), and CIEDE2000 color difference objective metrics. The evaluation results demonstrate that profiles A and B lead to similar saturation of quality at the higher bit rates, while profile C exhibits no saturation. Profiles B and C appear to be more dependent on TMOs used for the base layer compared to profile A.

Three-dimensional super-resolution structured illumination microscopy with maximum a posteriori probability image estimation

We introduce and demonstrate a new high performance image reconstruction method for super-resolution structured illumination microscopy based on maximum a posteriori probability estimation (MAP-SIM). Imaging performance is demonstrated on a variety of fluorescent samples of different thickness, labeling density and noise levels. The method provides good suppression of out of focus light, improves spatial resolution, and allows reconstruction of both 2D and 3D images of cells even in the case of weak signals. The method can be used to process both optical sectioning and super-resolution structured illumination microscopy data to create high quality super-resolution images.

Image Quality Evaluation in Security Imaging Systems

The subjective image quality of image or video information is a crucial item in security imaging systems. During the last five years our lab has tested and verified various approaches to the image compression for security purposes and the evaluation of subjective image quality. In the paper we discuss selected important facts related to the subjective image quality evaluation and we present some anomalous experimental behavior of image compression techniques. An object-defined approach is investigated and advantageous characteristics of chosen methods are deployed to achieve the optimal performance of the surveillance video coder. Among others, we propose to use the artificial neural network (ANN) to predict resulting image quality rating scores. The proposed quality assessment model has been trained and tested using a set of grayscale images distorted by selected image compression algorithms

Imaging photometer with a non-professional digital camera

Expensive measuring equipments and to some extent also professional digital still cameras are capable to capture scenes with high accuracy in representation of colors and consequently luminance. On the contrary, low-end equipments are in most of the cases adjusted by the manufacturer to produce visually attractive images with weak interest to reach high fidelity, measurement device close, reproduction of colors. In this paper we present a simple way how to use a consumer level digital camera as a 2D luminance meter or imaging photometer. Our approach is based mostly on measurement of spectral response curves of photosensitive elements and evaluation of output response from the raw image data captured by the camera. Utilization of these characteristics leads to a better calibration of the camera as a luminance meter. The performance improvement while using the image calibration data of the image sensor and objective lens is verified. The potential utilization of a consumer level digital camera as a substitute for expensive measuring devices is discussed.

Comparison of image reconstruction methods for structured illumination microscopy

Structured illumination microscopy (SIM) is a recent microscopy technique that enables one to go beyond the diffraction limit using patterned illumination. The high frequency information is encoded through aliasing into the observed image. By acquiring multiple images with different illumination patterns aliased components can be separated and a highresolution image reconstructed. Here we investigate image processing methods that perform the task of high-resolution image reconstruction, namely square-law detection, scaled subtraction, super-resolution SIM (SR-SIM), and Bayesian estimation. The optical sectioning and lateral resolution improvement abilities of these algorithms were tested under various noise level conditions on simulated data and on fluorescence microscopy images of a pollen grain test sample and of a cultured cell stained for the actin cytoskeleton. In order to compare the performance of the algorithms, the following objective criteria were evaluated: Signal to Noise Ratio (SNR), Signal to Background Ratio (SBR), circular average of the power spectral density and the S3 sharpness index. The results show that SR-SIM and Bayesian estimation combine illumination patterned images more effectively and provide better lateral resolution in exchange for more complex image processing. SR-SIM requires one to precisely shift the separated spectral components to their proper positions in reciprocal space. High noise levels in the raw data can cause inaccuracies in the shifts of the spectral components which degrade the super-resolved image. Bayesian estimation has proven to be more robust to changes in noise level and illumination pattern frequency.

Image quality and QoE in multimedia systems

The image and video sensing and processing systems are of high importance nowadays. The crucial parameter is image quality as a part of QoS or more recently QoE (Quality of Experience) as a subjective impact (perception). This invited paper deals with various qualitative aspects in imaging systems. The first part is devoted to the modeling of HVS and image quality evaluation. A model for prediction of perceived quality of degraded images is presented. This model is optimized while incorporating visual attention coordinates. In the following parts four different examples of qualitative impacts (distortions and artifacts) of image post-processing are listed and shown. The first one is the image enhancement, second one image restoration by iterative detectors based on the maximum a posteriori probability criterion, third one image interpolation and finally fourth one image compression and transmission.

Errata: Estimation of non-Gaussian noise parameters in the wavelet domain using the moment-generating function

We discuss methods for modeling and removal of noise in astronomical images. For its favorable properties, we exploit the undecimated wavelet representation and apply noise suppression in this domain. Usually, the noise analysis of the studied imaging system is carried out in the spatial domain. However, noise in astronomical data is non-Gaussian, and thus the noise model parameters need to be estimated directly in the wavelet domain.We derive equations for estimating the sample moments for non-Gaussian noise in the wavelet domain. We consider that the sample moments in the spatial domain are known from the noise analysis and that the model parameters are estimated by using the method of moments.

Meteor automatic imager and analyzer: system design and its parameters

A system for double station observation of the meteors now known as MAIA (Meteor Automatic Imager and Analyzer) is introduced in the paper. This system is an evolution of current analog solution. The system is based on the two stations with the gigabite ethernet cameras, sensitive image intensifiers and automatic processing of the recorded image data. The aim of such design is to capture and analyze images of meteors down to masses of fractions of gram. This paper presents the measured electrooptical characteristics of the particular components and the overall performance of the new digital system in the comparison to the current analog solution. At first the optimal settings of various parameters for each subsystem (primary lens, image intensifier, secondary lens and camera) are determined. Then the set of test images is captured and analyzed. The analysis of the images captured with both artificial and real targets verifies the suitability of the selected system design.