Acceleration of Fractal Image Compression using Characteristic Vector Classification

Rowshanbin, N.; Samavi, Shadrokh; Shirani, Shahram

doi:10.1109/ccece.2006.277529

Cited by 7 publications

(8 citation statements)

References 11 publications

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“…With a value of 32 classes for a 256 Â 256 image with four pixel domain overlap and 8 Â 8 range blocks, the average number of domains in the domain pool subset (the average number of matches found for each range) will be about 12 and hence eight parallel-processing units will be satisfactory configuration to find the best match among the recognized candidates. The proposed classification when compared to other classification techniques, such as [19], has higher simplicity while maintaining an acceptable level of accuracy. This gives the proposed classifier the advantage of being hardware realizable, while the produced PSNR is comparable with other acceleration techniques.…”

Section: Proposed Classification Techniquementioning

confidence: 93%

“…Some algorithms try to classify the range and domain pools into a number of groups. Classification is a scheme that many researchers have applied to the blocks of an image in order to restrain the search space and hence accelerate the coding process [18][19][20]. Then the search needs only be performed on a range and a number of domains, which belong to the same class.…”

Section: Fractal Acceleration Techniquesmentioning

confidence: 99%

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Real time fractal image coder based on characteristic vector matching

Samavi

Habibi

Shirani

et al. 2010

Image and Vision Computing

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Section: Proposed Classification Techniquementioning

confidence: 93%

Section: Fractal Acceleration Techniquesmentioning

confidence: 99%

Real time fractal image coder based on characteristic vector matching

Samavi

Habibi

Shirani

et al. 2010

Image and Vision Computing

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“…In addition, more threads implies in a higher cost on synchronization and mutex primitives. (Fu & Zhu, 2009;Jeng et al, 2009;Mitra et al, 1998;Qin et al, 2009;Revathy & Jayamohan, 2012;Rowshanbin et al, 2006;Sun & Wun, 2009;Vahdati et al, 2010). In particular, Revathy and Jayamohan (2012) proposed a dynamic preparation of a domain pool for each range block, instead of working with a set of static domains from the beginning of the execution (Revathy & Jayamohan, 2012).…”

Section: Resultsmentioning

confidence: 99%

“…Basically, the most alternatives try to reduce the coding time by reducing the search for the best-match block in a large domain pool (Fu & Zhu, 2009;Jeng et al, 2009;Mitra et al, 1998;Qin et al, 2009;Revathy & Jayamohan, 2012;Rowshanbin et al, 2006;Sun & Wun, 2009;Vahdati et al, 2010). Other possibilities consist in exploring the power of parallel architectures like nCUBE (Jackson & Blom, 1995), SIMD (Single Instruction Multiple Data) (Khan & Akhtar, 2013;Wakatani, 2012) processors and clusters (Righi, 2012;Qureshi & Hussain, 2008).…”

Section: Introductionmentioning

confidence: 99%

Exploiting Data-Parallelism on Multicore and SMT Systems for Implementing the Fractal Image Compressing Problem

Righi¹,

Rodrigues²,

Costa³

et al. 2016

CIS

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This paper presents a parallel modeling of a lossy image compression method based on the fractal theory and its evaluation over two versions of dual-core processors: with and without simultaneous multithreading (SMT) support. The idea is to observe the speedup on both configurations when changing application parameters and the number of threads at operating system level. Our target application is particularly relevant in the Big Data era. Huge amounts of data often need to be sent over low/medium bandwidth networks, and/or to be saved on devices with limited store capacity, motivating efficient image compression. Especially, the fractal compression presents a CPU-bound coding method known for offering higher indexes of file reduction through highly time-consuming calculus. The structure of the problem allowed us to explore data-parallelism by implementing an embarrassingly parallel version of the algorithm. Despite its simplicity, our modeling is useful for fully exploiting and evaluating the considered architectures. When comparing performance in both processors, the results demonstrated that the SMT-based one presented gains up to 29%. Moreover, they emphasized that a large number of threads does not always represent a reduction in application time. In average, the results showed a curve in which a strong time reduction is achieved when working with 4 and 8 threads when evaluating pure and SMT dual-core processors, respectively. The trend concerns a slow growing of the execution time when enlarging the number of threads due to both task granularity and threads management.

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“…To speed up the encoding, in [25], the classification features are used to classify the image blocks. Xing et al [26] used hierarchical partitioning to classify the domain pool. Fuzzy pattern classifier is utilized in [27], to classify the original image blocks.…”

Section: Related Work On Fractal Image Codingmentioning

confidence: 99%

A Review on Block Matching Motion Estimation and Automata Theory based Approaches for Fractal Coding

Kamble¹,

Thakur²,

Bajaj³

2016

IJIMAI

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---Fractal compression is the lossy compression technique in the field of gray/color image and video compression. It gives high compression ratio, better image quality with fast decoding time but improvement in encoding time is a challenge. This review paper/article presents the analysis of most significant existing approaches in the field of fractal based gray/color images and video compression, different block matching motion estimation approaches for finding out the motion vectors in a frame based on inter-frame coding and intra-frame coding i.e. individual frame coding and automata theory based coding approaches to represent an image/sequence of images. Though different review papers exist related to fractal coding, this paper is different in many sense. One can develop the new shape pattern for motion estimation and modify the existing block matching motion estimation with automata coding to explore the fractal compression technique with specific focus on reducing the encoding time and achieving better image/video reconstruction quality. This paper is useful for the beginners in the domain of video compression.

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Acceleration of Fractal Image Compression using Characteristic Vector Classification

Cited by 7 publications

References 11 publications

Real time fractal image coder based on characteristic vector matching

Real time fractal image coder based on characteristic vector matching

Exploiting Data-Parallelism on Multicore and SMT Systems for Implementing the Fractal Image Compressing Problem

A Review on Block Matching Motion Estimation and Automata Theory based Approaches for Fractal Coding

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