A hybrid image compression algorithm based on fractal coding and wavelet transform

Andreopoulos, Ioannis; Karayianris, Y.A.; Stouruaitis, T.

doi:10.1109/iscas.2000.855990

Cited by 16 publications

(12 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, seven workloads used the Scale Invariant Feature Transform (SIFT) salient point descriptor [22], which was deployed as compiled Matlab code with the Mathworks deploytool. These were selected as representative examples for image processing operations, amongst a larger pool of image and video processing experiments using wavelet transforms [23], [24], [25] and other codecs [26], [11], [12]. The total size of the inputs per workload is given in Fig.…”

Section: A Utilized Workloadsmentioning

confidence: 99%

Cloud Instance Management and Resource Prediction for Computation-as-a-Service Platforms

Doyle¹,

Giotsas²,

Anam³

et al. 2016

2016 IEEE International Conference on Cloud Engineering (IC2E)

View full text Add to dashboard Cite

Abstract-Computation-as-a-Service (CaaS) offerings have gained traction in the last few years due to their effectiveness in balancing between the scalability of Software-as-a-Service and the customisation possibilities of Infrastructure-as-a-Service platforms. To function effectively, a CaaS platform must have three key properties: (i) reactive assignment of individual processing tasks to available cloud instances (compute units) according to availability and predetermined time-to-completion (TTC) constraints; (ii) accurate resource prediction; (iii) efficient control of the number of cloud instances servicing workloads, in order to optimize between completing workloads in a timely fashion and reducing resource utilization costs. In this paper, we propose three approaches that satisfy these properties (respectively): (i) a service rate allocation mechanism based on proportional fairness and TTC constraints; (ii) Kalman-filter estimates for resource prediction; and (iii) the use of additive increase multiplicative decrease (AIMD) algorithms (famous for being the resource management in the transport control protocol) for the control of the number of compute units servicing workloads. The integration of our three proposals into a single CaaS platform is shown to provide for more than 27% reduction in Amazon EC2 spot instance cost against methods based on reactive resource prediction and 38% to 60% reduction of the billing cost against the current state-of-the-art in CaaS platforms (Amazon Lambda and Autoscale).

show abstract

Section: A Utilized Workloadsmentioning

confidence: 99%

Cloud Instance Management and Resource Prediction for Computation-as-a-Service Platforms

Doyle¹,

Giotsas²,

Anam³

et al. 2016

2016 IEEE International Conference on Cloud Engineering (IC2E)

View full text Add to dashboard Cite

show abstract

“…Coding process requires rather long time for domain code book generation and domain -range matching process. Comparing with the available present Hybrid Fractal -Wavelet compression [10], [11],this proposed ILFIC compression algorithm provides high compression ratio with symmetric encodingdecoding algorithms without compromising much on the quality of picture.…”

Section: Introductionmentioning

confidence: 98%

Iteration less Wavelet-Fractal Image Compression Applicable in Cellular Mobile Communication System

Sheeba¹,

Rahiman²

2016

IJCA

View full text Add to dashboard Cite

Fractal image compression is a an active area of research with new promising technique that will work very effectively in areas where we have to deal with a huge size of data .In Fractal compression major challenge is the exhaustive comparison needed in the encoding stage. In this paper a method of iteration free-detail space fractal image compression is proposed, in which time of encoding is reduced without compromising much on the quality of image and this algorithm guarantees a high compression ratio. This is a hybrid algorithm of fractal mathematics and wavelet Transform. When comparing with the existing hybrid techniques the advantage of this proposed method is, only the approximation space undergoes exhaustive comparison and thereby it guarantees higher speed than the existing Hybrid techniques. IFS (Iterated function system) of detail space are calculated using the result of approximation space. Experimental results show that in the proposed method Computational over head is considerably reduced and maintains a good tradeoff between compression ratio and quality of image. Resultant image is resolution independent, which is the one of the properties of fractal image compression. Since this new technique guarantees, high compression ratio and low encoding time, it may work very well in mobile communication, especially in face book application.

show abstract

“…The image compression mechanism that is proposed by the Joint Photographic Expert Group (JPEG) [2] is todays still image lossy compression standard and it is used for natural images. It combines block implementation of the Discrete Cosine Transform (DCT) quantisation technique and then Huffman coding.…”

Section: Introductionmentioning

confidence: 99%

Automatic production of quantisation matrices based on perceptual modelling of wavelet coefficients for grey scale images

Al-Hudhud

Ibrahim

Al-Akaidi

2010

Image and Vision Computing

View full text Add to dashboard Cite

A hybrid image compression algorithm based on fractal coding and wavelet transform

Cited by 16 publications

References 9 publications

Cloud Instance Management and Resource Prediction for Computation-as-a-Service Platforms

Cloud Instance Management and Resource Prediction for Computation-as-a-Service Platforms

Iteration less Wavelet-Fractal Image Compression Applicable in Cellular Mobile Communication System

Automatic production of quantisation matrices based on perceptual modelling of wavelet coefficients for grey scale images

Contact Info

Product

Resources

About