An original blurriness assessment method for video frames is presented. In the first place two measurements
are performed uniformly on the whole frame. The first quantifies the perceptive contrast experienced by the
human eye, the second uses anisotropic diffusion to estimate a possible detail loss due to blurriness. Secondarily
two further indices are devised, each one suitable to a particular sort of image content. The first is dedicated
to main, uniform objects, where smoothed borders are deemed to be the main source of blurriness perception.
First, a technique is devised to extract all edges, including the smoothest ones. Then the width of such edges
is measured, and more weight is given to long than to short ones. The second index measures the activity of
textured areas, trying to detect blurriness inside the base texture elements. The four devised indices enable
automatic quantification of the strength of blurriness and some hints at its origin. In particular, some new
results have been achieved in the ability to automatically distinguish natural blurriness, present in the image
content, from undesired one, introduced during encoding and processing.
It has long been known that the human visual system (HVS) has a nonlinear response to luminance. This
nonlinearity can be quantified using the concept of just noticeable difference (JND), which represents the minimum
amplitude of a specified test pattern an average observer can discern from a uniform background. The JND
depends on the background luminance following a threshold versus intensity (TVI) function.
It is possible to define a curve which maps physical luminances into a perceptually linearized domain. This
mapping can be used to optimize a digital encoding, by minimizing the visibility of quantization noise. It is also
commonly used in medical applications to display images adapting to the characteristics of the display device.
High dynamic range (HDR) displays, which are beginning to appear on the market, can display luminance
levels outside the range in which most standard mapping curves are defined. In particular, dual-layer LCD
displays are able to extend the gamut of luminance offered by conventional liquid crystals towards the black
region; in such areas suitable and HVS-compliant luminance transformations need to be determined. In this
paper we propose a method, which is primarily targeted to the extension of the DICOM curve used in medical
imaging, but also has a more general application. The method can be modified in order to compensate for the
ambient light, which can be significantly greater than the black level of an HDR display and consequently reduce
the visibility of the details in dark areas.
A method is presented to measure the intensity of the blocking artefact in compressed pictures or video frames.
First, a way is devised to artificially introduce pure blocking, which closely resembles the real one subsequent
to JPEG compression. Then a modified no-reference measurement is proposed that requires less computations
than other formerly presented methods, permits to take into account the whole image or frame area, and is
not affected by interlaced video. Some first experiments indicate that the measured values relate closely to the
introduced blockiness effect. The robustness of the metric to the influence of other typical JPEG artefacts is
also checked. Further, the effect on blockiness of some enhancement strategies is measured. Pictures enhanced
with methods introducing the most severe blockiness are found to have the highest value of the proposed metric.
Finally the problem of blockiness measurement in video sequences is addressed. In this case the blocking grid is
no longer regular. In fact, blocks of different size could be used in encoding, and single blocks could be shifted
in referenced (P and B) frames due to motion compensation. A method is devised for grid detection.
Liquid crystal displays (LCDs) are replacing analog film in radiology and reducing diagnosis times. Their typical dynamic range, however, can be too low for some applications, and their poor ability to reproduce low-luminance areas represents a critical drawback. The black level of an LCD can be drastically improved by stacking two liquid crystal panels in series. In this way the global transmittance is the pointwise product of the transmittances of the two panels and the theoretical dynamic range is squared. Such a high dynamic range (HDR) display also permits the reproduction of a larger number of gray levels, increasing the bit depth of the device. The two panels, however, are placed at a small distance from each other due to mechanical constraints, and this introduces a parallax error when the display is observed off-axis. A complex, spatially adaptive algorithm is therefore necessary to generate the images used to drive the two panels. We describe the characteristics of a prototype dual-layer HDR display and discuss the issues involved in the image-splitting algorithms. We propose some solutions and analyze their performance, giving a measure of the capabilities and limitations of the device.
We explore the calibration of a high luminance range, dual-layer, liquid crystal display (LCD) prototype. The
operation of the prototype is done by splitting a high luminance resolution image (graylevel > 28) into two 8-bit
depth components and sending these images to the two liquid crystal panels stacked over the backlight module.
By interpolation of a small set of luminance data gathered using a specialized luminance probe, the look-up table
of graylevel pairs of front/back layer LCD and the corresponding luminance values can be generated. To display
images, we fit an extended DICOM model to the interpolated luminance table which is adjustable for graylevel
and luminance depth. A dynamic look up table is generated in which for each luminance there are several graylevel
pair candidates. We show results for one possible calibration strategy involving the pair selection criterion. By
selecting the pair that maximizes back-layer smoothness, the images with arbitrary graylevel and luminance
depth can be then displayed with equal perceptual distance between luminance levels, while minimizing parallax
effects. Other possible strategies that minimize glare and noise are also described. The results can be used for
high luminance range display performance characterization and for the evaluation of its clinical significance.
Liquid crystal displays (LCD) are replacing analog film in radiology and permit to reduce diagnosis times. Their
typical dynamic range, however, can be too low for some applications, and their poor ability to reproduce low
luminance areas represents a critical drawback. The black level of an LCD can be drastically improved by
stacking two liquid crystal panels in series. In this way the global transmittance is the pointwise product of the
transmittances of the two panels and the theoretical dynamic range is squared. Such a high dynamic range (HDR)
display also permits the reproduction of a larger number of gray levels, increasing the bit depth of the device.
The two panels, however, are placed at a small distance one from each other due to mechanical constraints, and
this introduces a parallax error when the display is observed off-axis. A complex, spatially-adaptive algorithm
is therefore necessary to generate the images used to drive the two panels.
In this paper, we describe the characteristics of a prototype dual-layer HDR display and discuss the issues
involved in the image splitting algorithms. We propose some solutions and analyze their performance, giving a
measure of the capabilities and limitations of the device.
We virtually restore faded black and white photographic prints by the method of decomposing the image into
a smooth component that contains edges and smoothed homogeneous regions, and a rough component that may
include grain noise but also fine detail. The decomposition into smooth and rough components is achieved using
a rational filter. Two approaches are considered; in one, the smooth component is histogram-stretched and then
gamma corrected before being added back to a homomorphically filtered version of the rough component; in the
other the image is initially gamma corrected and shifted towards white. Each approach presents improvements
with respect to the previously separately explored techniques of gamma correction alone, and the stretching
of the smooth component together with the homomorphical filtering of the rough component, alone. After
characterizing the image with the help of the scatter plot of a 2D local statistic of the type (local intensity, local
contrast), namely (local average, local standard deviation), the effects of gamma correction are studied as the
effects on the scatter plot, on the assumption that the quality of the image is related to the distribution of data
on the scatter plot. Also, the correlation coefficient between the local average and the local deviation on the one
hand, and the global average of the image play important descriptor roles.
A method for the detection of cracks in old paper photographs is presented. Cracks in photographic prints usually
result from a folding of the paper support of the photograph; they often extend across the entire image, along a
preferred orientation. A first clue we exploit for crack detection is the fact that old prints have a characteristic
sepia hue, due to aging and to the type of processing used at the time; a break of the gelatin exposes the white
support paper; likewise, a break of the paper causes a black region in the digitized image. Thus, cracks are
usually achromatic; this fact can be used for their detection on a color space with an explicit hue component.
A series of parallel microcracks that run along the direction of a main crack usually result as well; even though
the gelatin may not be broken, the folds corresponding to these microcracks cause a set of image discontinuities,
observable at a high-enough resolution. In an interactive process, the user indicates the ends of the crack on
the frame of the photo and the algorithm detects the crack pixels. In addition to color, the algorithm uses a
multidirectional, multiresolution Gabor approach and mathematical morphology. The resulting method provides
crack detection with good performance, as evidenced by the corresponding Receiver Operating Characteristics
We propose a solution for the computer-aided reconstruction
of strip-cut shredded documents. First of all, the visual content
of the strips is automatically extracted and represented by a
number of numerical features. Usually, the pieces of different pages
have been mixed. A grouping of the strips belonging to a same page
is thus realized by means of a clustering operator, to ease the successive
matching performed by a human operator with the help of a
The dynamic range of an image is defined as the ratio between the maximum and minimum luminance value
it contains. This value in real images can be several thousands or even millions, whereas the dynamic range
of consumer imaging devices rarely exceeds 100; therefore some processing is needed in order to display a high
dynamic range image correctly. Global operators map each pixel individually with the same nonlinear function;
local operators use spatially-variant functions in order to achieve a higher quality. The lower computational cost
of global operators makes them attractive for real-time processing; the nonlinear mapping can however attenuate
the image details. In this paper we define an expression which gives a quantitative measure of this artifact, and
compare the performance of some commonly used operators. We show that a modified logarithm we propose has
a satisfactory performance for a wide class of images, and has a theoretical justification based on some properties
of the human visual system. We also introduce a method for the automatic tuning of the parameters of our
system, based on the statistics of the input image. We finally compare our method with others proposed in the
In the forensics and investigative science fields there may arise the need of reconstructing documents which have been destroyed by means of a shredder. In a computer-based reconstruction, the pieces are described by numerical features, which represent the visual content of the strips. Usually, the pieces of different pages have been mixed. We propose an approach for the reconstruction which performs a first clustering on the strips to ease the successive matching, be it manual (with the help of a computer) or automatic. A number of features, extracted by means of image processing algorithms, have been selected for this aim. The results show the effectiveness of the features and of the proposed clustering algorithm.
We propose a new algorithm to digitally restore vintage photographic prints affected by foxing and water blotches. It semiautomatically recovers the defects utilizing the features of the stains. The restoration process enhances the residual image information still present in the area. It is composed of three different steps: inpainting, additive-multiplicative (A-M) modeling, and interpolation.
In this paper a general architecture for the computer-aided reconstruction of strip-cut shredded documents is presented. The matching of the remnants is performed on the base of the visual content of the strips, described by means of automatically extracted numerical features. A clustering approach is adopted in order to reduce progressively the dimension of the sets of remnants in which the exhaustive search for the matching need to be performed.
We propose a novel method for motion analysis in video sequences.
It extends the co-occurrence matrix concept for texture analysis
to the temporal domain. The approach proved to be versatile in the
sense of targeting different motion analysis tasks. An application
of the method is in the compact representation of video sequences,
in particular temporal texture patterns.
One of the most common tasks in image processing is to change the resolution of a picture. In this paper we
present a new nonlinear method for interpolating digital images, which is particulary effective in the rendition of
edges in natural and synthetic input. The algorithm is spatial variant and applies the warped distance (WaDi)
concept, generalizing the technique to a two dimensional problem, which requires a non-separable approach.
It consists of three separate stages. First of all the original image is analyzed to detect its local gradient
characteristics; then edge asymmetry is computed at each output pixel position according to the WaDi technique,
and it is compared to a reference sigmoidal edge; the local edge asymmetry straightforwardly determines the
warping factor which is applied to the bi-dimensional space of the image; eventually the actual interpolation is
performed applying a conventional interpolator such as the linear or bicubic ones. The resulting interpolation
method gives an output which does not present the usual blurring typical of images processed with linear
interpolators and at the same time preserves the regularity of resized edges avoiding jagging artifacts. Moreover,
the method adapts for zooming by a rational scaling factor. The paper is organized as follows. In the first
section we introduce the problem of zooming digital images; the second section describes the state of the art; we
continue describing the proposed method; then we propose a possible extension of the method to process color
images; we end showing some examples of images interpolated with our method, and comparing these results
with what can be obtained zooming the same input with other interpolators.
In this paper we present a digital image enhancement technique which relies on the application of a nonlinear operator within the Retinex approach. The basic idea of this approach is to separate the illumination and reflectance components of the image, so that by reducing the contribution of the former it is possible to effectively control the dynamic range of the latter. However, its behaviour critically depends on the quality of the illumination estimation process, so that either annoying artifacts are generated, or very complex operators have to be used, which may prevent the use of this method in several cost- and time-sensitive applications. Our method is able to provide, thanks to the use of a suitable nonlinear operator, good quality, artifacts-free images at a limited computational complexity.
Fixed matrix displays require digital interpolation algorithms to adapt the input spatial format to the output matrix. Interpolation techniques usually employed for this purpose exploit linear kernels designed to preserve the spectral content (anti aliasing), but this generates smooth edges, which result in unpleasant text images where sharpness is essential. By contrast, interpolation kernels designed to preserve sharpness introduce geometrical distortions in the scaled text (e.g. nearest neighbor interpolation). This paper describes an interpolation algorithm which, compared to linear techniques, aims to increase the sharpness of interpolated text while preserving its geometrical regularity. The basic idea is to differentiate the processing for text and non-text pixels. Firstly, a binary text map is built. By using morphological constraints it is possible to form a similar text map in the output domain that preserves the general text regularity. Finally, output text pixel positions are used to control a nonlinear interpolator (based on the Warped Distance approach) that is able to generate both step and gradual luminance profiles, thus enabling the algorithm to locally change its behavior. A general sharpness control is provided as well, which permits to range from a two-level text (maximum sharpness) to a smoother output image (traditional linear interpolation).
We present in this paper a novel and effective system for removing blotches in old film sequences. In particular we propose a new very efficacious detection method: it is able to yield a high correct detection rate while minimizing, at the same time, the false alarm rate. Moreover, it is very efficient also in presence of slow motion, since it exploits both temporal and spatial features of the blotches. Adaptive Block Matching is used for the blotch correction step.
In the field of video technology for surveillance applications it is often necessary to cope with the phenomenon of illumination variations. In fact, if not compensated, such variations can falsely trigger the change detection module that detects intrusions in video surveillance systems, thus affecting their reliability. Many studies have been made to solve the change detection problem under varying illumination conditions. Most of the published methods, however, rely only on the luminance information. The algorithm proposed in this paper exploits independently the information of each band of the RGB color space of the video sequences, thus producing a change detection algorithm that is more robust to illumination variations. These illumination variations are globally modeled by the so- called Von Kries model (also known as diagonal scaling model). This model is generally used to solve the color constancy problems, where conformance to a reference image illumination has to be guaranteed, like in color image retrieval applications. The use of this model is motivated by its low computational cost and by the interest of studying the relationship between color constancy and change detection. Based on practical experiments which confirm the interest in this method, new and more robust change detection algorithms are expected to be designed. In addition, the paper proposes the use of an iterative scheme whose aim is to improve the results obtained in the change detection module, and which is independent of this module, i.e., it can be used with other change detection schemes. It will be shown that the iteration can improve the quality of the final change mask, thus permitting to obtain a more effective change detection scheme.
This paper proposes an FPGA architecture for a videowall image processor. To create a videowall, a set of high resolution displays is arranged in order to present a single large image or smaller multiple images. An image processor is needed to perform the appropriate format conversion corresponding to the required output configuration, and to properly enhance the image contrast. Input signals either in the interlaced or in the progressive format must be managed. The image processor we propose is integrated into two different blocks: the first one implements the deinterlacing task for a YCbCr input video signal, then it converts the progressive YCbCr to the RGB data format and performs the optional contrast enhancement; the other one performs the format conversion of the RGB data format. Motion-adaptive vertico-temporal deinterlacing is used for the luminance signal Y; the color difference signals Cb and Cr instead are processed by means of line average deinterlacing. Image contrast enhancement is achieved via a modified Unsharp Masking technique and involves only the luminance Y. The format conversion algorithm is the bilinear interpolation technique employing the Warped Distance approach and is performed on the RGB data. Two different subblocks have been considered in the system architecture since the interpolation is performed column-wise and successively row- wise.
A new processing scheme for large high-resolution displays such as Videowalls is proposed in this paper. The scheme consists in a deinterlacing, an interpolation and an optional enhancement algorithm; its hardware implementation requires a low computational cost. The deinterlacing algorithm is motion- adaptive. A simple hierarchical three-level motion detector provides indications of static, slow and fast motion to activate a temporal FIR filter, a three-tap vertico-temporal median operator and a spatial FIR filter respectively. This simple algorithm limits the hardware requirements to three field memories plus a very reduced number of algebraic operations per interpolated pixel. Usually linear techniques such as pixel repetition or the bilinear method are employed for image interpolation, which however either introduce artifacts (e.g. blocking effects) or tend to smooth edges. A higher quality rendition of the image is obtained by the concept of the Warped Distance among the pixels of an image. The computational load of the proposed approach is very small if compared to that of state-of-the-art nonlinear interpolation operators. Finally the contrast enhancement algorithm is a modified Unsharp Masking technique: a polynomial function is added to modulate the sharpening signal, which allows to discriminate between noise and signal and, at the same time, provides an appropriate amplification to low-contrast image details.
Rational filters are extended to multichannel signal processing and applied to the image interpolation problem. The proposed nonlinear interpolator exhibits desirable properties, such as, edge and details preservation. In this approach the pixels of the color image are considered as 3-component vectors in the color space. Therefore, the inherent correlation which exists between the different color components is not ignored; thus, leading to better image quality than those obtained by component-wise processing. Simulations show that the resulting edges obtained using vector rational filters (VRF) are free from blockiness and jaggedness, which are usually present in images interpolated using especially linear, but also some nonlinear techniques, e.g. vector median hybrid filters (VFMH).
In the unsharp masking approach for image enhancement,
a fraction of the highpass filtered version of the image is added to the original image to form the enhanced version. The method is simple, but it suffers from two serious drawbacks. First, it enhances the contrast in the darker areas perceptually much more strongly than that in the lighter areas. Second, it enhances the noise and/or digitization effects, particularly in the darker regions, resulting in visually less pleasing enhanced images. In general, noise can be suppressed with lowpass filters, which are associated with the blurring of the edges. On the other hand, contrast can be enhanced with highpass filters, which are associated with noise amplification. A reasonable solution, therefore, is to use suitable nonlinear filters which combine the features of both highpass and lowpass filters. This paper outlines several new methods of unsharp masking based on the use of such nonlinear filters. Computer simulations have verified the superior results obtained using these filters. In addition, a new measure of contrast enhancement is introduced which quantitatively supports the improvement obtained using the proposed methods.
A nonlinear filtering technique for the preprocessing of very low contrast images has been applied to optical profilometry, as an attempt to improve the accuracy of the measurement of objects in harsh conditions. The technique is based on the application of a nonlinear architecture composed of linear Laplacian filters followed by quadratic filters which detect correlated elements. The above sequence of operators results in efficient highpass filtering, keeping at the same time the signal-to-noise ratio within acceptable limits. When applied to highly transparent or weakly diffusive surfaces, the preelaboration technique has largely improved the accuracy of the profilometer. In this paper the preelaboration technique is presented. In particular, the influence of the nonlinear image elaboration on the overall system performance is discussed.