37244
Data Hiding in Film Grain
This paper presents a data hiding technique based on a new compression enhancement called Film Grain Technology. Film grain is a mid-frequency noise-like pattern naturally appearing in imagery captured on film. The Film Grain Technology is a method for modeling and removing the film grain, thus enhancing the compression efficiency, and then using the model parameters to create synthetic film grain at the decoder. We propose slight modifications to the decoder that enable the synthetic film grain to represent metadata available at the decoder. We examine a number of implementation approaches and report results of fidelity and robustness experiments.
25090129
Comments on "A translation- and scale-invariant adaptive wavelet transform"
Xiong et al. presented a translationand scale-invariant adaptive wavelet transform. We show that their renormalized signal (pre-wavelet transform) is already translation- and scale-invariant. Thus, it is not just invariant under the wavelet transform. We give a necessary and sufficient condition for renormalized signals to be affine transform invariant.
6315327
Reversible data embedding using a difference expansion
Reversible data embedding has drawn lots of interest recently. Being reversible, the original digital content can be completely restored. We present a novel reversible data-embedding method for digital images. We explore the redundancy in digital images to achieve very high embedding capacity, and keep the distortion low.
14734990
Reversible data-embedding with a hierarchical structure
Reversible data-embedding embeds data into a digital content (such as digital image, audio or video) such that an authorized party could decode the embedded data and completely restore the original content. In this paper, we present a hierarchically structured, multi-layer algorithm for digital images. Its basic component is an invertible sharpen filter called the difference expansion. The embedding capacity of this algorithm is substantially higher than results reported in previous reversible data-embedding algorithms. The visual quality of images after data-embedding is also among the best in the literature.
12486810
A new class of biorthogonal wavelet systems for image transform coding
We construct general biorthogonal Coifman wavelet systems, a new class of compactly supported biorthogonal wavelet systems with vanishing moments equally distributed for a scaling function and wavelet pair. A time-domain design method is employed and closed-form expressions for the impulse responses and the frequency responses of the corresponding dual filters are derived. The resulting filter coefficients are all dyadic fractions, which is an attractive feature in the realization of multiplication-free discrete wavelet transform. Even-ordered systems in this family are symmetric, which correspond to linear-phase dual filters. In particular, three filterbanks (FBs) in this family are systematically verified to have competitive compression potential to the 9-7 tap biorthogonal wavelet FB by Cohen et al., which is currently the most widely used one in the field of wavelet transform coding. In addition, the proposed FB's have much smaller computational complexity in terms of floating-point operations required in transformation, and therefore indicate a better tradeoff between compression performance and computational complexity.
206622046
Fingerprint Alignment Using Similarity Histogram
The performance of fingerprint matching algorithm relies heavily on the accuracy of fingerprint alignment. Falsely aligning two feature sets extracted from two finger images of a fingerprint will increase the false rejection rate (FRR). In order to improve the performance of fingerprint matching algorithm, we present a new fingerprint alignment algorithm called similarity histogram approach (SHA). First, we calculate the local similarity matrix based on minutiae and associate ridges between two fingerprints. Then, similarity histograms of transformation parameters are constructed from local similarity matrix. In the end, the optimal transformation parameters are obtained using a statistical method. Experimental results on FVC databases show that our method is effective and reliable.
18528442
Better Reliability Assessment and Prediction through Data Clustering
This paper presents a new approach to software reliability modeling by grouping data into clusters of homogeneous failure intensities. This series of data clusters associated with different time segments can be directly used as a piecewise linear model for reliability assessment and problem identification, which can produce meaningful results early in the testing process. The dual model fits traditional software reliability growth models (SRGMs) to these grouped data to provide long-term reliability assessments and predictions. These models were evaluated in the testing of two large software systems from IBM. Compared with existing SRGMs fitted to raw data, our models are generally more stable over time and produce more consistent and accurate reliability assessments and predictions.
4621823
On the Testable Implications of Causal Models with Hidden Variables
The validity of a causal model can be tested only if the model imposes constraints on the probability distribution that governs the generated data. In the presence of unmeasured variables, causal models may impose two types of constraints: conditional independencies, as read through the d-separation criterion, and functional constraints, for which no general criterion is available. This paper offers a systematic way of identifying functional constraints and, thus, facilitates the task of testing causal models as well as inferring such models from data.
37559798
Measurement and continuous improvement of software reliability throughout software life-cycle
Abstract Software reliability is one of the most important aspects of software quality. However, most of the existing techniques focus on the system testing and other late stages of development life-cycle. Therefore, they are not effective for early problem identification and reliability improvement. This paper describes our recent work in establishing predictive linkage between software reliability and other entities that we can measure and control early in the development cycle, and using such predictive relations to drive continuous reliability improvement.
34795143
Automatic Fingerprint Identification Using Cluster Algorithm