Sparse approximation with adaptive dictionary for image prediction

Türkan, Mehmet; Guillemot, Christine

doi:10.1109/icip.2009.5413923

Cited by 20 publications

(17 citation statements)

References 7 publications

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“…The K-SVD is adopted for dictionary training in the proposed S/DCT algorithm. The sparse representation of a motion compensated residual signal was studied in the dictionary based video coding [19], [22]. The dictionary can be designed to contain typical structural patterns of a residual signal, e.g.…”

Section: Review Of Residual Coding With Sparse Representationmentioning

confidence: 99%

Sparse/DCT (S/DCT) Two-Layered Representation of Prediction Residuals for Video Coding

Kang

Gabbouj

Kuo

2013

IEEE Trans. on Image Process.

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Abstract-In this paper, we propose a cascaded sparse/DCT (S/DCT) two-layer representation of prediction residuals, and implement this idea on top of the state-of-the-art high efficiency video coding (HEVC) standard. First, a dictionary is adaptively trained to contain featured patterns of residual signals so that a high portion of energy in a structured residual can be efficiently coded via sparse coding. It is observed that the sparse representation alone is less effective in the R-D performance due to the side information overhead at higher bit rates. To overcome this problem, the DCT representation is cascaded at the second stage. It is applied to the remaining signal to improve coding efficiency. The two representations successfully complement each other. It is demonstrated by experimental results that the proposed algorithm outperforms the HEVC reference codec HM5.0 in the Common Test Condition.Index Terms-ρ domain rate model, discrete cosine transform (DCT), high efficiency video coding (HEVC), multilayered coding, overcomplete dictionary based video coding, residual coding, sparse representation.

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Section: Review Of Residual Coding With Sparse Representationmentioning

confidence: 99%

Sparse/DCT (S/DCT) Two-Layered Representation of Prediction Residuals for Video Coding

Kang

Gabbouj

Kuo

2013

IEEE Trans. on Image Process.

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“…In reference [5], the template candidates are viewed as the basis and the orthogonal matching pursuit algorithm [14] is used to find the coefficients for each basis, so that the linear combination of the bases is regarded as the predictor of the target template. In our method, the orthogonal matching pursuit algorithm is also used.…”

Section: A Dominating Template Candidates and Corresponding Weightingsmentioning

confidence: 99%

“…The average of the block candidates is used as the predictor of the target block in [4]. Instead of plain average, the method in [5] predicts the target block by performing linear combination of the chosen block candidates. The weighting coefficients are determined by using the template candidates and the template of the target block.…”

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confidence: 99%

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Inter-frame Prediction with Fast Weighted Low-rank Matrix Approximation

Huang¹,

Hsiao²

2013

International Journal of Electronics and Telecommunications

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Abstract-In the field of video coding, inter-frame prediction plays an important role in improving compression efficiency. The improved efficiency is achieved by finding predictors for video blocks such that the residual data can be close to zero as much as possible. For recent video coding standards, motion vectors are required for a decoder to locate the predictors during video reconstruction. Block matching algorithms are usually utilized in the stage of motion estimation to find such motion vectors. For decoder-side motion derivation, proper templates are defined and template matching algorithms are used to produce a predictor for each block such that the overhead of embedding coded motion vectors in bit-stream can be avoided. However, the conventional criteria of either block matching or template matching algorithms may lead to the generation of worse predictors. To enhance coding efficiency, a fast weighted low-rank matrix approximation approach to deriving decoder-side motion vectors for inter frame video coding is proposed in this paper. The proposed method first finds the dominating block candidates and their corresponding importance factors. Then, finding a predictor for each block is treated as a weighted low-rank matrix approximation problem, which is solved by the proposed column-repetition approach. Together with mode decision, the coder can switch to a better mode between the motion compensation by using either block matching or the proposed template matching scheme.

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“…The work in [7] averaged multiple template matching predictors, including larger and directional templates, resulting in more than 15% coding efficiency in H.264/AVC codec. Sparse approximation such as matching pursuit [8] and orthogonal matching pursuit [9] have been proposed to change the matching between the template and candidate into the distance between template and the linear combination of candidates. Also, other matching criterions such as residue coding cost combining with mean square error (MSE) have also been used to replace the sum of absolute distance between template and candidates [10].…”

Section: Introductionmentioning

confidence: 99%

Template matching for image prediction: A game-theoretical approach

Lin

Gao

Liu

2012

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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This paper presents a game-theoretical approach to provide a framework for optimal template selection in image prediction. Image prediction is an effective tool for coding still images and intra pictures in videos. Template matching algorithms which use neighboring blocks of the prediction target as templates have been widely used for image prediction. The assumption of these approaches is that the template has similar textural structures as the prediction target. Up to now these approaches all use pre-fixed templates for all prediction targets. However, in real images, these fixed templates are very likely to contain textures that are not or are not significant in the prediction targets and these insignificant textures introduce larger prediction residues. In this paper, we propose a coalitional game in which every pixel is treated as a player and tries to seek partners to form a coalition to capture the textural structure. By forming a coalition, every player in the coalition can obtain a gain of improving the ability of capturing the textural structure of coalition while incurring a cost of introducing textural variance within the coalition. Experimental results show that the proposed game-theoretical approach outperforms the conventional pre-fixed template matching prediction up to 2dB coding gain.

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Sparse approximation with adaptive dictionary for image prediction

Cited by 20 publications

References 7 publications

Sparse/DCT (S/DCT) Two-Layered Representation of Prediction Residuals for Video Coding

Sparse/DCT (S/DCT) Two-Layered Representation of Prediction Residuals for Video Coding

Inter-frame Prediction with Fast Weighted Low-rank Matrix Approximation

Template matching for image prediction: A game-theoretical approach

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