Showing papers by "Giovanni Ramponi published in 2013"

Detection and measurement of the blocking artifact in decoded video frames

Abstract: A method for measuring the blocking artifact in video frames is presented, which is capable of detecting this artifact even outside a regular geometric structure, that is, on moving objects in frames encoded with prediction-based techniques. Information-theoretic measures are integrated with models of the human perception, to account for the visibility of the artifact on different image content. A final metric is produced that yields coherent values on variously degraded versions derived from different originals. No information is required on the encoding procedure that originated the artifact; this makes this method suitable to operate on the decoded version of the frames, at the final stage of the video processing chain. Experiments show that the metric has a good correlation with subjective scores and possesses some additional desirable properties.

Guest editorial: special issue on algorithms and architectures for real-time image and video enhancement

Abstract: Recent advances in real-time image and video enhancement are enabling innovations in a broad range of applications including biomedicine, intelligent transportation, driving assistance, consumer electronics, telecommunication, robotics and surveillance. These innovations encompass complexity-aware algorithms and new hardware– software (HW–SW) architectures and aims at (1) improved visualization performance; (2) acceleration of processing (real-time instead of off-line computation); and (3) complexity reduction to meet demands involving device size, power consumption, cost of target applications such as battery-powered mobile/wearable devices, or low-cost large volume markets. This special issue presents nine papers covering different real-time algorithms and cost-efficient architectures for several image/video enhancement techniques. The accepted papers are from different international institutions located in North and South America, Europe and Asia. The research on image/video enhancement used to be mainly focused on multimedia, consumer or telecom applications. However, this special issue demonstrates the growing interest for image/video enhancement techniques to fields such as biomedicine (capsule endoscopy and neuroscience test), robotics for automation in agriculture, automotive driving assistance, and aerial surveillance. The discussed techniques include object tracking, image and video compression, edge extraction/detection for image analysis, anomaly detection, lighting conditions improvement, and contrast enhancement. The first paper by Khan et al. presents a subsamplingbased image compressor for capsule endoscopic system which is aimed at reducing the chip area and power consumption, while maintaining an acceptable video quality. A low-complexity algorithm, suitable for VLSI implementation, is developed around some special features of endoscopic images and consists of a differential pulse code modulation followed by Golomb–Rice coding. An image corner clipping algorithm is also presented. The reconstructed images are verified by medical doctors for acceptability. Compared to other transform-based algorithms targeted to capsule endoscopy, the proposed raster-scanbased scheme performs very strongly with a compression ratio of 80% and a very high reconstruction PSNR (over 45 dB). The second paper by Armato et al. also deals with biomedical-related applications. This work is focused on exploring and comparing several photometric normalization techniques to improve eye gaze tracking (EGT) systems during light changes. EGT is developed for scientific exploration in controlled environments where it is used in ophthalmology, neurology, psychology, and related areas to study oculomotor characteristics and abnormalities, and their relation to cognition and mental states. The illumination is one of the most restrictive limitations in EGT, due to the problem of pupil center estimation during illumination S. Saponara (&) Dip. Ingegneria della Informazione, Universita di Pisa, via G. Caruso 16, 56122 Pisa, Italy e-mail: sergio.saponara@iet.unipi.it

Causes and subjective evaluation of blurriness in video frames

Abstract: A method to automatically assess the seriousness of the blurriness artifact in video frames displayed on a state-of-the-art TV monitor is presented. Different types of the artifact are identified, depending on the stage of the video chain where they are originated, namely, blur produced during acquisition, post-processing and encoding. Every type is observed to produce slightly different effects on the frame and, more importantly, to affect image quality differently, so that distinguishing among types is necessary to perform an appropriate restoration. Two main metrics are therefore introduced for classifying the type of blurriness and, when useful, measuring its strength. Particular care was taken in distinguishing the intentional background blur, which does not cause an actual degradation in the frame quality. The appropriateness of the method in classifying the artifact and predicting the subjective frame quality was verified in the experiments.

Gray-level mapping in advanced medical displays: Determining the collective and individual visual response of the users

Abstract: The DICOM Grayscale Standard Display Function (GSDF) is widely used in the medical imaging field to map the image values into luminance emitted by the display. However, the DICOM GSDF is not accurate at very dark luminance levels, and this causes a loss of visibility in the details if an image is viewed on the novel High Dynamic Rance display devices such as the ones based on the Dual Layer LCD technology. In this paper, we describe two experiments we performed in order to measure the eye sensitivity at low luminance levels and thus propose a correction. The first experiment was performed in controlled viewing conditions (dark room and fixed viewing distance) that reproduce as closely as possible those defined in the DICOM specification. The method we chose is a “staircase” procedure and uses the recently proposed “2AFC with denoising” technique, combined with a maximum-likelihood method. The results confirmed that the DICOM model overestimates the eye sensitivity at very low luminance levels. The second experiment was conducted with free viewing distance and with the lights both off and on, in order to simulate more realistic operating conditions. We used the “QUEST” algorithm to conduct the test and to compute the results, using available open-source software. The results show that the free viewing distance can improve the visibility of the details in the dark portions, because the observers tend to move closer to the display. At the same time, the ambient light has a severe impact on the observer's performance in the dark portion, but a negligible, and sometimes even slightly positive effect in the bright portions. The final target of this work is to propose a modified Display Function which offers a good performance in the entire luminance range of an HDR display and can be personalized according to the individual characteristics of each observer and to the level of ambient light.

An efficient new edge preserving technique for removal of salt and pepper noise

Abstract: This paper proposes an efficient procedure for removal of salt and pepper noises from the noisy images on the basis of their local edge preserving filters. This algorithm consists of two major stages. In the first stage, the maximum and minimum pixel value in the the corrupted image is used to select noisy pixels or noise free pixels and then in second stage, local edge preserving filters are used on the basis of noisy pixel detected and the nature of its neighboring pixels in the selected window. Comparing the obtained results with other computationally simple noise removal techniques, our proposed algorithm gives much better qualitative and quantitative performance. Due to its simplicity and low computational cost, our method is suitable for its application in many real time situations.

Effects of different prediction schemes on lossless invisible watermarking

Abstract: In view of high data embedding capacity and many real time applications, we have proposed a reversible invisible watermarking algorithm. The technique is used to embed a set of watermark data in an image using a one pass embedding process and later recovering the original image without any loss, after the extraction of watermark. Main motivation behind this work is the usage of two different prediction schemes-Weighted Causal Average and Context Based Image Compression Algorithm, to obtain two images similar to the original image, and using the error pattern resulted from the two predicted images to embed data depending upon the nature of image. Based on this error pattern, binary data is embedded in an image using two different embedding schemes- Bijective Mirror Mapping technique and Histogram Shifting Algorithm, resulting in better embedding capacity or payload capacity and better PSNR than other reversible one-pass watermarking algorithms mentioned in literature.

A low complex algorithm for interpolation as well as lossless compression of natural images

Abstract: This paper presents a new generic algorithm for image interpolation as well as lossless image coding. Main motivation behind the work is to reduce computational complexity involved in using Least Square Error Minimization (LS). The proposed method down samples the given image to its quarter size and then to its (1/16)th size. For each downsampled image, the least Square predictors are then obtained corresponding to pixels belonging to each bin. Thus, these predictors are used to synthetically generate a set of optimal predictors corresponding to each bin of the original image. Our proposed algorithm thus reduces 60% to 70% of computational complexity. We also observed that proposed algorithm gives insignificant loss in terms of compression ratio as compared with some of the previous works reported in literature.