Image compression using an edge adapted redundant dictionary and wavelets

Peotta, Lorenzo; Granai, L.

doi:10.1016/j.sigpro.2005.05.023

Cited by 34 publications

(24 citation statements)

References 25 publications

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“…Finally, since it has been shown that redundant decompositions are mostly beneficial at low rates, the proposed scheme can offer an efficient coding solution for the base layer in scalable applications. Similarly to hybrid image coding schemes [28], enhancement layers based on spherical wavelets, for example, could nicely complement the proposed scheme for high-bit-rate coding. …”

Section: Discussionmentioning

confidence: 94%

Progressive Coding of 3-D Objects Based on Overcomplete Decompositions

Tošić

Frossard

2006

IEEE Trans. Circuits Syst. Video Technol.

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Abstract-This paper presents a progressive coding scheme for 3-D objects, based on overcomplete signal expansions on the 2-D sphere. Due to increased freedom in the basis construction, redundant expansions have shown interesting approximation properties in the decomposition of signals with multidimensional singularities organized along embedded submanifolds. We propose to map simple 3-D models on 2-D spheres and then to decompose the signal over a redundant dictionary of oriented and anisotropic atoms living on the sphere. The signal expansion is computed iteratively with a matching pursuit algorithm, which greedily selects the most prominent components of the 3-D model. The decomposition therefore inherently represents a progressive stream of atoms, which is advantageously used in the design of scalable representations. An encoder is proposed that compresses the stream of atoms by adaptive coefficient quantization and entropy coding of atom indexes. Experimental results show that the novel coding strategy outperforms state-of-the-art progressive coders in terms of distortion, mostly at low bit rates. Furthermore, since the dictionary is built on structured atoms, the proposed representation simultaneously offers an increased flexibility for easy stream manipulations. We finally illustrate that advantage in the design of a view-dependent transmission scheme.

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Section: Discussionmentioning

confidence: 94%

Progressive Coding of 3-D Objects Based on Overcomplete Decompositions

Tošić

Frossard

2006

IEEE Trans. Circuits Syst. Video Technol.

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“…In the last few years, the emerging attention is to enlarge common orthogonal bases through the design of suitable redundant dictionaries positioned as an interesting alternative. The latter can be a mixture of orthogonal bases [21] and/or dictionaries [22]. Such merging approach aims to design domains where each sub-dictionary is suitable for representing one of the signal components.…”

Section: Transform Domainsmentioning

confidence: 99%

Tuned depth signal analysis on merged transform domain for view synthesis in free viewpoint systems

Chaieb

Sebai

Ghorbel

2015

IET Image Processing

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Completely embedded in the 3D era, depth maps coding becomes a must in order to favour 3D admission to different fields of application, ranging from video games to medical imaging. This study presents a novel depth coding approach that, after a decimation step favouring the foreground, decomposes depth maps onto a set of sparse coefficients and redundant mixed discrete cosine and B-splines atoms highly correlated to depth maps piece-wise linear nature. Depth decomposition searches the best rate/distortion tradeoff through minimisation of an adaptive cost function, where its weight parameter is manipulated according to depth homogeneity. The bigger the parameter is, the more the sparsity is favoured at the expense of synthesis quality. Furthermore, handled distortion measure of the cost function quantifies the effect of depth maps coding on rendered views quality. The experiments show the relevance of the proposed method, able to obtain considerable tradeoffs between bitrate and synthesised views distortion.

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“…This is why adaptive approaches based on minimizing the τ norm where 0 ≤ τ ≤ 1 have elicited intense interest since the early 1990s [48,23], in a context extending well beyond that of source separation [83,43,90,29,96]. Despite the advantages of a simple, efficient numerical implementation, transforms are far from ideal in terms of sparse representation when the dictionary is redundant.…”

Section: Properties and Limitationsmentioning

confidence: 99%