Arcangelo Ranieri Bruna

Digital video stabilization through curve warping techniques

The widespread diffusion of hand-held devices with video recording capabilities requires the adoption of reliable digital Stabilization methods to enjoy the acquired sequences without disturbing jerkiness. In order to effectively get rid of the unwanted camera movements, an estimate of the global motion between adjacent frames is necessary. This paper presents a novel approach for estimating the global motion between frames using a curve warping technique known as dynamic time warping. The proposed algorithm guarantees robustness also in presence of sharp illumination changes and moving objects.

A Robust Block-Based Image/Video Registration Approach for Mobile Imaging Devices

Digital video stabilization enables to acquire video sequences without disturbing jerkiness by compensating unwanted camera movements. In this paper, we propose a novel fast image registration algorithm based on block matching. Unreliable motion vectors (i.e., not related with jitter movements) are properly filtered out by making use of ad-hoc rules taking into account local similarity, local “activity,” and matching effectiveness. Moreover, a temporal analysis of the relative error computed at each frame has been performed. Reliable information is then used to retrieve inter-frame transformation parameters. Experiments on real cases confirm the effectiveness of the proposed approach even in critical conditions.

A robust video stabilization system by adaptive motion vectors filtering

Digital video stabilization allows to acquire video sequences without disturbing jerkiness, removing unwanted camera movements. In this paper we propose a novel fast video stabilization algorithm based on block matching of local motion vectors. Some of these vectors are properly filtered out by making use of ad-hoc rules taking into account local similarity, local ldquoactivityrdquo and matching effectiveness. Also a temporal analysis of the relative error computed at each frame has been achieved. Reliable information are then used to retrieve inter-frame transformation parameters. Experiments on real cases confirm the effectiveness of the proposed approach even in critical conditions.

Fast method for noise level estimation and integrated noise reduction

This paper describes a fast method for noise level estimation and denoising. Specifically, we address the problem of estimating the standard deviation of additive white Gaussian noise in digital images; the computed value is used to reduce Gaussian noise and eliminate defective pixels in a raw digital image. The method is particularly suitable for implementation in low power mobile devices with imaging capabilities such as camera phones, as well as digital still cameras (DSC).

Natural scenes classification for color enhancement

An innovative solution for automatic color rendition of natural digital images is presented. It is based on an adaptive color correction, driven by a natural scene classifier designed over a wide database of natural digital images used as reference. The perceived quality of the processed images is globally closer to the expected color rendition for a great part of observers, without evident artifacts. A visual comparison with two wide diffused automatic and semi-automatic color enhancement tools is also presented.

Color space transformations for digital photography exploiting information about the illuminant estimation process

The color reproduction accuracy is a key factor to the overall perceived image quality in digital photography. In this framework, both the illuminant estimation process and the color correction matrix concur in the formation of the overall perceived image quality. To the best of our knowledge, the two processes have always been studied separately, thus ignoring the interactions between them. We investigate here these interactions, showing how the color correction transform amplifies the illuminant estimation errors. We demonstrate that incorporating knowledge about the illuminant estimation behavior in the optimization of the color correction matrix makes it possible to alleviate the error amplification. Different strategies to improve color accuracy under both perfect and imperfect white point estimations are investigated, and the experimental results obtained with a digital camera simulator are reported.

Noise Reduction for CFA Image Sensors Exploiting HVS Behaviour

This paper presents a spatial noise reduction technique designed to work on CFA (Color Filtering Array) data acquired by CCD/CMOS image sensors. The overall processing preserves image details using some heuristics related to the HVS (Human Visual System); estimates of local texture degree and noise levels are computed to regulate the filter smoothing capability. Experimental results confirm the effectiveness of the proposed technique. The method is also suitable for implementation in low power mobile devices with imaging capabilities such as camera phones and PDAs.

First Quantization Matrix Estimation From Double Compressed JPEG Images

One of the most common problems in the image forensics field is the reconstruction of the history of an image or a video. The data related to the characteristics of the camera that carried out the shooting, together with the reconstruction of the (possible) further processing, allow us to have some useful hints about the originality of the visual document under analysis. For example, if an image has been subjected to more than one JPEG compression, we can state that the considered image is not the exact bitstream generated by the camera at the time of shooting. It is then useful to estimate the quantization steps of the first compression, which, in case of JPEG images edited and then saved again in the same format, are no more available in the embedded metadata. In this paper, we present a novel algorithm to achieve this goal in case of double JPEG compressed images. The proposed approach copes with the case when the second quantization step is lower than the first one, exploiting the effects of successive quantizations followed by dequantizations. To improve the results of the estimation, a proper filtering strategy together with a function devoted to find the first quantization step, have been designed. Experimental results and comparisons with the state-of-the-art methods, confirm the effectiveness of the proposed approach.

Color correction pipeline optimization for digital cameras

Abstract. The processing pipeline of a digital camera converts the RAW image acquired by the sensor to a representation of the original scene that should be as faithful as possible. There are mainly two modules responsible for the color-rendering accuracy of a digital camera: the former is the illuminant estimation and correction module, and the latter is the color matrix transformation aimed to adapt the color response of the sensor to a standard color space. These two modules together form what may be called the color correction pipeline. We design and test new color correction pipelines that exploit different illuminant estimation and correction algorithms that are tuned and automatically selected on the basis of the image content. Since the illuminant estimation is an ill-posed problem, illuminant correction is not error-free. An adaptive color matrix transformation module is optimized, taking into account the behavior of the first module in order to alleviate the amplification of color errors. The proposed pipelines are tested on a publicly available dataset of RAW images. Experimental results show that exploiting the cross-talks between the modules of the pipeline can lead to a higher color-rendition accuracy.

Coding techniques for CFA data images

In this paper we present a comparison between different approaches to CFA (colour filter array) image encoding. We show different performance offered by a new algorithm based on a vector quantization technique, JPEG-LS, a low complexity encoding standard and classical JPEG. We also show the effects of CFA image encoding on the colour reconstructed images by a typical image generation pipeline. A discussion about the computational complexity and memory requirement of the different encoding approaches is also presented.