Image Compression Using SVD

Indukuri, Kishore Varma; Ambekar, Anurag; Sureka, Ashish

doi:10.1109/iccima.2007.386

Cited by 57 publications

(17 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this particular case, the calculation methods of IFCDT, IFCDTS and IFCDTL defined in Equations (18), (20) and (21) are not valid, because there is no information about target texture, target location and target size, which are essential factors in proposed metrics. We know that target tracking in the images without target information is very difficult.…”

Section: Calculation Methods Of the Three New Metrics When The Targetmentioning

confidence: 99%

“…Many of the global gray-level metrics in the literature seem to be designed with that in mind. The standard deviation of an image and the entropy of its histogram are two of the simplest measures of an image [17][18][19].…”

Section: Global Metricsmentioning

confidence: 99%

“…Since the characteristics of a single image and the characteristics of an image sequence are different, it is not surprising that the metrics for single images are not best suited for measuring image sequences. The existing image sequence measure methods, such as peak signal-to-noise ratio (PSNR) [16,17], mean square errors (MSE) [18][19][20], mean structural similarity (MSSM) [21], have been proposed in the fields of image coding and image communication. These metrics, different from that in the field of automatic target tracking, focus on measuring information losses in video compression, therefore they have not been applied to evaluate the performance of automatic target tracking algorithms in image sequences.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Image Sequence Measures for Automatic Target Tracking

Diao¹,

Mao²,

Zheng³

et al. 2012

PIER

View full text Add to dashboard Cite

Abstract-In the field of automatic target recognition and tracking, traditional image metrics focus on single images, ignoring the sequence information of multiple images. We show that measures extracted from image sequences are highly relevant concerning the performances of automatic target tracking algorithms. To compensate the current lack of image sequence characterization systems from the perspective of the target tracking difficulties, this paper proposes three new metrics for measuring image sequences: inter-frame change degree of texture, inter-frame change degree of target size and inter-frame change degree of target location. All are based on the fact that inter-frame change is the main cause interfering with target tracking in an image sequence. As image sequences are an important type of data in the field of automatic target recognition and tracking, it can be concluded that the work in this paper is a necessary supplement for the existing image measurement systems. Experimental results reported show that the proposed metrics are valid and useful.

show abstract

Section: Calculation Methods Of the Three New Metrics When The Targetmentioning

confidence: 99%

Section: Global Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Image Sequence Measures for Automatic Target Tracking

Diao¹,

Mao²,

Zheng³

et al. 2012

PIER

View full text Add to dashboard Cite

show abstract

“…In these cases the HOSVD is applied. Without additional surface parameters the HOSVD has the simple SVD form [3] [4].…”

Section: Introductionmentioning

confidence: 99%

HOSVD Based Method for Surface Data Approximation and Compression

Szeidl

Rudas

Rövid

et al. 2008

2008 International Conference on Intelligent Engineering Systems

View full text Add to dashboard Cite

The main aim of this paper is to introduce a method for approximating surfaces given by a set of discrete points with possible additional parameters. The method uses the Higher Order Singular Value Decomposition based on canonical form of two-variable TP (Tensor Product) functions. Except of approximation abilities of this principle, the paper focuses on the compression properties of the method, as well. The method is able to achieve high compression rate in the input data by keeping the error at remarkable lower level.

show abstract

“…Karhunen-Loeve Transform (KLT), and Singular Value Decomposition (SVD) [Richardson, 2010;Bovik, 2010;Jizheng et al, 2009;Prasantha et al, 2007].…”

Section: Transform Coding (Tc)mentioning

confidence: 99%

Enhanced Computation Time for Fast Block Matching Algorithm

Ahmed

Hussain

Al‐Jumeily

2011

2011 Developments in E-Systems Engineering

View full text Add to dashboard Cite

i ACKNOWLEDGMENTSMy thanks go firstly, as it should always be, to Allah who gave me the energy, health, and courage to spend a lot of determination and time until I completed this work.My most sincere gratitude and appreciation to my supervisor Dr Abir Hussain for all her patience, valuable advice, discussions, convincing arguments and more during the life of this thesis, I wish her all the best for the future. I also owe a great debt to Dr. Dhiya Al-Jumeily for his support, advice and encouragement from the beginning until now and I would like to thank him for his constructive comments on my thesis. Motion compensated predictive coding is the main coding tool for removing temporal redundancy of video sequences and it typically accounts for 50-80% of the video encoding complexity. This technique has been adopted by all of the existing international video coding standards. It assumes that the current frame can be locally modelled as a translation of the reference frames. The practical and widely method used to carry out motion compensated prediction is block matching algorithm. In this method, video frames are divided into a set of non-overlapped macroblocks; each target macroblock of the current frame is compared with the search area in the reference frame in order to find the best matching macroblock. This will carry out displacement vectors that stipulate the movement of the macroblocks from one location to another in the reference frame. Checking all these locations is called full Search, which provides the best result. However, this algorithm suffers from long computational time, which necessitates improvement. Several methods of Fast Block Matching algorithm were developed to reduce the computation complexity.This thesis focuses on two classifications: the first is called the lossless block matching algorithm process, in which the computational time required to determine the matching macroblock of the full search is decreased while the resolution of the predicted frames is the same as for the full search. The second is called the lossy block matching algorithm process, which reduces the computational complexity effectively but the search result's quality is not the same as for the full search.

show abstract

Image Compression Using SVD

Cited by 57 publications

References 5 publications

Image Sequence Measures for Automatic Target Tracking

Image Sequence Measures for Automatic Target Tracking

HOSVD Based Method for Surface Data Approximation and Compression

Enhanced Computation Time for Fast Block Matching Algorithm

Contact Info

Product

Resources

About