Giancarlo Calvagno

Demosaicing With Directional Filtering and a posteriori Decision

Most digital cameras use a color filter array to capture the colors of the scene. Downsampled versions of the red, green, and blue components are acquired, and an interpolation of the three colors is necessary to reconstruct a full representation of the image. This color interpolation is known as demosaicing. The most effective demosaicing techniques proposed in the literature are based on directional filtering and a posteriori decision. In this paper, we present a novel approach to this reconstruction method. A refining step is included to further improve the resulting reconstructed image. The proposed approach requires a limited computational cost and gives good performance even when compared to more demanding techniques

Image coding by block prediction of multiresolution subimages

The redundancy of the multiresolution representation has been clearly demonstrated in the case of fractal images, but it has not been fully recognized and exploited for general images. Fractal block coders have exploited the self-similarity among blocks in images. We devise an image coder in which the causal similarity among blocks of different subbands in a multiresolution decomposition of the image is exploited. In a pyramid subband decomposition, the image is decomposed into a set of subbands that are localized in scale, orientation, and space. The proposed coding scheme consists of predicting blocks in one subimage from blocks in lower resolution subbands with the same orientation. Although our prediction maps are of the same kind of those used in fractal block coders, which are based on an iterative mapping scheme, our coding technique does not impose any contractivity constraint on the block maps. This makes the decoding procedure very simple and allows a direct evaluation of the mean squared error (MSE) between the original and the reconstructed image at coding time. More importantly, we show that the subband pyramid acts as an automatic block classifier, thus making the block search simpler and the block matching more effective. These advantages are confirmed by the experimental results, which show that the performance of our scheme is superior for both visual quality and MSE to that obtainable with standard fractal block coders and also to that of other popular image coders such as JPEG.

Lossless compression of video using temporal information

In this paper, we consider the problem of lossless compression of video by taking into account temporal information. Video lossless compression is an interesting possibility in the line of production and contribution. We propose a compression technique which is based on motion compensation, optimal three-dimensional (3-D) linear prediction and context based Golomb-Rice entropy coding. The proposed technique is compared with 3-D extensions of the JPEG-LS standard for still image compression. A compression gain of about 0.8 bit/pel with respect to static JPEG-LS, applied on a frame-by-frame basis, is achievable at a reasonable computational complexity.

Color image demosaicking: An overview

Demosaicking is the process of reconstructing a full-resolution color image from the sampled data acquired by a digital camera that apply a color filter array to a single sensor. This paper discusses the need of a color filter array and presents a survey of several techniques proposed to demosaicking. A comparison between the different methods is also provided, discussing their performances.

Regularization Approaches to Demosaicking

Demosaicking is the process of reconstructing a full resolution color image from the sampled data acquired by a digital camera that apply a color filter array to a single sensor. In this paper, we propose a regularization approach to demosaicking, making use of some prior knowledge about natural color images, such as smoothness of each single color component and correlation between the different color channels. Initially, a quadratic strategy is considered and a general approach is reported. Then, an adaptive technique is analyzed, in order to improve the reconstruction near the edges and the discontinuities of the image. This is performed using a novel strategy that avoids computational demanding iterations. The proposed approach provides good performances and candidates itself for many applications. Moreover, since the response of the pixel sensors can be taken into account, it can handle nonideal acquisition devices.

A Depth Image Coder Based on Progressive Silhouettes

An efficient compression of depth maps proves to be a crucial element in the transmission and storage of 3-D scenes. However, the peculiarities of geometry information make the traditional coding paradigms for natural images less effective for the coding of depth images. The letter presents a novel coding scheme that employs an oversegmentation of the input depth image into a huge set of small regions. These regions are then fused together according to the target number of objects that the algorithm needs to identify in the representation. This procedure is iterated more than once generating several refinement layers that permit obtaining a progressively-increasing quality in the scene. Experimental results show that in most cases the proposed approach reaches a better coding performance with respect to previous coding methods.

A multiresolution approach to spike detection in EEG

A technique is proposed for the automatic detection of spikes in electroencephalograms (EEG). A multiresolution approach and a non-linear energy operator are exploited. The signal on each EEG channel is decomposed into three subbands using a non-decimated wavelet transform. Each subband is analyzed by using a non-linear energy operator, in order to detect peaks. A decision rule detects the presence of spikes in the EEG, relying upon the energy of the three subbands. The effectiveness of the proposed technique was confirmed by analyzing both test signals and EEG layouts.

Joint denoising and interpolation of depth maps for MS Kinect sensors

Infrared structured light sensors are widely employed for control applications, gaming, acquisition of dynamic and static 3D scenes. Recent developments have lead to the availability on the market of low-cost sensors which prove to be extremely sensitive to noise, light conditions, materials, the surface nature of the objects, and their distance from the camera. As a matter of fact, accurate denoising and interpolation strategies are needed. The paper presents a quality enhancement strategy for depth maps targeting low-cost IR structured light sensors. The approach has been tested using the MS Xbox Kinect device in both indoor and outdoor scenarios under different light conditions.

Modeling of subband image data for buffer control

We develop an adaptive scheme for quantization of subband or transform coded frames in a typical video sequence coder. Using a generalized Gaussian model for the subband or transform coefficients, we present a procedure to determine the optimum dead-zone quantizer for a given entropy of the quantizer output symbols. We find that, at low bit rates, the dead-zone quantizer offers better performance than the uniform quantizer. The model is used to develop an adaptive procedure to update the quantizer parameters on the basis of the state of a channel buffer with constant output rate and variable input rate. We compare the accuracy of the generalized Gaussian model in predicting the actual bit rate to that achievable using the simpler and more common Laplacian model. Experimental results show that the generalized Gaussian model has superior performance than the Laplacian model, and that it can be effectively used in a practical scheme for buffer control.

Demosaicing Based Onwavelet Analysis of the Luminance Component

Most color digital cameras apply a color filter array to capture the scene, requiring an interpolation of the subsampled color components to obtain the full resolution image. Therefore, if reconstruction is not performed correctly, noticeable artifacts are produced, which result more evident in the detailed parts of the image. In this paper we propose a demosaicing algorithm based on directional filtering that uses a novel approach to locate the details of the image. To this purpose, edge-estimation is performed on the luminance component of the image in order to give a more reliable information, and the properties of the wavelet transform are used to estimate the edge directions. Experimental results proved the effectiveness of this approach, giving high performance in PSNR, and good estimates of image details too.