Proc. SPIE. 6507, Multimedia on Mobile Devices 2007
KEYWORDS: Digital signal processing, 3D acquisition, Video acceleration, Detection and tracking algorithms, Video, Denoising, Signal processing, Video processing, Algorithm development, Motion estimation
The recent development of in the field of embedded systems has enabled mobile devices with significant computation
power and long battery life. However, there are still a limited number of video applications for such platforms. Due to
high computational requirements of video processing algorithms, an intensive assembler optimization or even hardware
design is required to meet the resource constraints of the mobile platforms. One example of such challenging video
processing problem is video denoising.
In this paper, we present a software implementation of a state-of-the-art video denoising algorithm on a mobile
computational platform. The chosen algorithm is based on the three-dimensional discrete cosine transform (3D DCT)
and block-matching. Apart from its architectural simplicity, algorithm allows the computational scalability due to the
"sliding window"-style processing. In addition, main components of this algorithm are 8-point DCT and block matching
which can be efficiently calculated with hardware acceleration of the modern DSP.
Our target platform is the OMAP Innovator development kit, a dual processor environment including ARM 925 RISC
general purpose processor (GPP) and TMS320C55x digital signal processor (DSP). The C55x DSP offers a hardware
acceleration support for computing of the DCT and block-matching intensively used in the chosen denoising algorithm.
Hardware acceleration can offer a significant "speed-up" in comparison to assembler optimization of source codes. The
results demonstrate a possibility to implement an efficient video denoising algorithm on a mobile computational
platform with limited computational resources.
In this contribution, we explore the best basis paradigm for in feature extraction. According to this paradigm, a library of bases is built and the best basis is found for a given signal class with respect to some cost measure. We aim at constructing a library of anisotropic bases that are suitable for the class of 2-D binarized character images. We consider two, a dyadic and a non-dyadic generalization scheme of the Haar wavelet packets that lead to anisotropic bases. For the non-dyadic case, generalized Fibonacci p-trees are used to derive the space division structure of the transform. Both schemes allow for an efficient O(NlogN) best basis search algorithm.
The so built extended library of anisotropic Haar bases is used in the problem of optical character recognition. A special case, namely recognition of characters from very low resolution, noisy TV images is investigated. The best Haar basis found is then used in the feature extraction stage of a standard OCR system. We achieve very promising recognition rates for experimental databases of synthetic and real images separated into 59 classes.
The paper is devoted to design, fast implementation and applications of a family of 8-points integer orthogonal transforms based on a parametric matrix. A unified algorithm for their efficient computations is developed. Derived fast transforms have close coding gain performance to the optimal Karhunen-Loeve transform for the first order Markov process. Among them are also such that closely approximate the DCT-II and, at the same time, have a larger coding gain. For a particular set of parameters, integer transforms with reduced computational complexity are obtained. The comparative analysis of these transforms with the DCT-II in the framework of image denoising and video coding is performed.