Image compressed sensing recovery using intra-block prediction

Mandache, Diana; Akbari, Ali; Trocan, Maria

doi:10.1109/telfor.2015.7377574

Cited by 2 publications

(1 citation statement)

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“…The analysis and synthesis sparse signal modelling has led to the design effective algorithms for many image-processing applications, such as compression [2][3][4][5][6][7][8] and solving inverse problems [9][10][11][12][13][14][15][16][17][18][19][20][21][22]. A straightforward application of the sparse signal modelling in the field of image processing has been image compression due to providing a compact representation of the signal.…”

Section: Introductionmentioning

confidence: 99%

On the Application of Dictionary Learning to Image Compression

Akbari¹,

Trocan²

2020

Coding Theory

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Signal models are a cornerstone of contemporary signal and image-processing methodology. In this chapter, a particular signal modelling method, called synthesis sparse representation, is studied which has been proven to be effective for many signals, such as natural images, and successfully used in a wide range of applications. In this kind of signal modelling, the signal is represented with respect to dictionary. The dictionary choice plays an important role on the success of the entire model. One main discipline of dictionary designing is based on a machine learning methodology which provides a simple and expressive structure for designing adaptable and efficient dictionaries. This chapter focuses on direct application of the sparse representation, i.e. image compression. Two image codec based on adaptive sparse representation over a trained dictionary are introduced. Experimental results show that the presented methods outperform the existing image coding standards, such as JPEG and JPEG2000.

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