Large Domain Pool Research Articles

Fractal video sequences coding with region-based functionality

In this paper, we explore the fractal video sequences coding in the context of region-based functionality. Since the main drawback of fractal coding is the high computational complexity, some schemes are proposed to speed up the encoding process. As fractal encoding essentially spends most time on the search for the best-matching block in a large domain pool, this paper firstly ameliorates the conventional CPM/NCIM method and then applies a new hexagon block-matching motion estimation technology into the fractal video coding. The images in the video sequences are encoded region by region according to a previously-computed segmentation map. Experimental results indicate that the proposed algorithm spends less encoding time and achieves higher compression ratio and compression quality compared with the conventional CPM/NCIM method.

A Fast Fractal Video Coding Algorithm Using Cross-Hexagon Search for Block Motion Estimation

We propose a novel fractal video coding method using fast block-matching motion estimation to overcome the drawback of the time-consuming character in the fractal coding. As fractal encoding essentially spends most time on the search for the best-matching block in a large domain pool, search patterns and the center-biased characteristics of motion vector distribution have large impact on both search speed and quality of block motion estimation. In this paper, firstly, we propose a new hexagon search algorithm (NHEXS), and, secondly, we ameliorate, by using this NHEXS, the traditional CPM/NCIM, which is based on Fisher's quadtree partition. This NHEXS uses two cross-shaped search patterns as the first two initial steps and large/small hexagon-shaped patterns as the subsequent steps for fast block motion estimation (BME). NHEXS employs halfway stop technique to achieve significant speedup on sequences with stationary and quasistationary blocks. To further reduce the computational complexity, NHEXS employs modified partial distortion criterion (MPDC). Experimental results indicate that the proposed algorithm spends less encoding time and achieves higher compression ratio and compression quality compared with the traditional CPM/NCIM method.

Spatial correlation genetic algorithm for fractal image compression

Abstract Fractal image compression explores the self-similarity property of a natural image and utilizes the partitioned iterated function system (PIFS) to encode it. This technique is of great interest both in theory and application. However, it is time-consuming in the encoding process and such drawback renders it impractical for real time applications. The time is mainly spent on the search for the best-match block in a large domain pool. In this paper, a spatial correlation genetic algorithm (SC-GA) is proposed to speed up the encoder. There are two stages for the SC-GA method. The first stage makes use of spatial correlations in images for both the domain pool and the range pool to exploit local optima. The second stage is operated on the whole image to explore more adequate similarities if the local optima are not satisfied. With the aid of spatial correlation in images, the encoding time is 1.5 times faster than that of traditional genetic algorithm method, while the quality of the retrieved image is almost the same. Moreover, about half of the matched blocks come from the correlated space, so fewer bits are required to represent the fractal transform and therefore the compression ratio is also improved.

Fast fractal image encoding using one-norm of normalised block

Abstract The baseline fractal image encoding with full search typically requires a very long encoding time, which is essentially spent on searching for the best-matched block to an input range block in a large domain pool. In this paper, one-norm of normalised block is first utilized to avoid the excessive search, in which the search process might be early terminated, and thus remaining domain blocks could be safely discarded. Then the encoding schemes proposed recently by the authors are used to further reduce the search space and improve the decoded image quality. Experiments show that, for three popular 512 × 512 test images, the proposed algorithm can averagely reduce the runtime by about 40 times while there is averagely the PSNR gain of 0.91 dB, in comparison with the baseline fractal algorithm. Besides, when combined with the kick-out condition and the zero contrast prediction proposed recently by Lai et al. [Lai CM, Lam KM, Siu WC. A fast fractal image coding based on kick-out and zero contrast conditions. IEEE Trans Image Process 2003:11;1398–403], the combined algorithm further reduced the runtime by about 10% while maintaining the same PSNR exactly as that of the proposed algorithm.

Fast fractal image compression using spatial correlation

Abstract Fractal image compression is time consuming in the encoding process. The time is essentially spent on the search for the best-match block in a large domain pool. In this paper, the spatial correlations in both the domain pool and the range pool are utilized to reduce the searching space. With this technique, the encoding speed is 2.6 times faster than that of the full search method while the quality of the retrieved image is almost the same. Moreover, since the searching space is limited to the matched blocks of the previous range blocks, fewer bits are required to represent the transform. The bit rate is thus improved by about 20%.

A fast encoding algorithm for fractal image compression

The major drawback of the fractal image compression is the high encoding complexity to find the best match between a range block and a large pool of domain blocks. This paper presents a fast fractal-encoding algorithm based on the law of cosines. The number of domain blocks searched to find the best match for each range block is safely reduced by eliminating the ineligible domain blocks using the law of cosines. Simulation results show that the proposed algorithm can produce a completely identical fractal code to that of the exhaustive search in reduced time.

Fractal image compression based on intrablock variance distribution and vector quantization

In the encoding phase of fractal image compression, most of the time is taken in finding the closest match between each range block and a large pool of domain blocks. We use the intrablock variance dis- tributions of domain blocks to reduce the search space. For finding a close match, we need search only the domain blocks whose maximal intrablock variance quadrants are at the same corner as the range block. Thus, we reduce the number of transforms applied on each domain block from eight to two. We also adopt the longest-distance-first vector quantization scheme to divide the large pool of domain blocks into clus- ters. Thus, the number of domain blocks to be searched is also reduced. The experimental results show that our algorithm can reduce encoding time with only slight loss of quality. © 2002 Society of Photo-Optical Instrumen- tation Engineers. (DOI: 10.1117/1.1510743)

Creating usable models for re‐usable data — managing electronic project specification information

Small architectural firms (those that employ less than six people) account for an estimated 4525 or 74% of the total number of practices in the UK (RIBA, 2000), and market research has shown that there is a poor uptake of existing project specification information technologies in this sector. We present the results of an analysis of the ways that small practices currently create, manage and communicate project specification information and an overview of our research into new models to support these processes. The research has focused on the creation and population of appropriate, re‐usable information models and we describe these in the context of other modelling initiatives in the Architecture, Engineering and Construction (AEC) industry. The research has resulted in the definition of a large pool of domain specific data objects, which have the potential to provide a generic information resource for the industry. The Architectural Informatics Group, in conjunction with the National Building Specification (NBS), have implemented the results of this research through the development of a new commercial software tool for use in small design practices in the UK.

Low-complexity fractal-based image compression using two-stage search strategy

A fractal image compression scheme is presented which incorporates the vector quantizer (VQ) to reduce the computational complexity in the encoding phase. The high-detail blocks of an input image are approximated by a fast fractal coding, and the low-detail blocks are encoded by the VQ technique. In fractal image coding, most of the time is spent on finding a close match between a range block and a large pool of domain blocks. We propose a two-stage search algorithm to reduce the search space of the domain pool. First, a fast and simple binary pattern matching algorithm is proposed to extract a small pool of candidate domains, and then the gray-level-matching algorithm is used to find the best match from the candidates. In addition, a variable block-size segmentation method is used in our coding system to further improve the image quality. It is shown that the average encoding time is short, and the average speedup factor of 45 is achieved, as compared to the full search fractal coding. Simulation results also show that the proposed scheme can achieve better image quality and higher compression ratio than other fast fractal-based coding schemes.

Fast fractal image block coding based on local variances

In fractal image block coding, most of the time is spent on finding a close match between a range block and a large pool of domain blocks. For a large image, this effect becomes aggravated as the domain pool increases exponentially. We propose using the local variances of domain blocks to reduce the search space. By sorting the contracted domain pool according to their local variances and defining an acceptance criterion for a close match, we can confine all the potential close matches to a relatively small sized window to limit the search space. The encoding time can hence be shortened with the decoded image quality as good as that using the full search method. The speedup can be over ten times depending on the complexity of encoded images.