Image compression via sparse reconstruction

Yuan, Yuan; Au, Oscar C.; Zheng, Amin; Yang, Haitao; Tang, Ketan; Sun, Wenxiu

doi:10.1109/icassp.2014.6853954

Cited by 7 publications

(5 citation statements)

References 17 publications

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“…Recently, a new sparse dictionary learning model was proposed by imposing a compressibility constraint on the coefficients [25]. In [50], an image is partitioned to basis blocks and non-basis blocks, and non-basis blocks are compressed using the dictionary trained by basis blocks. However, there is still much room to improve the compression efficiency of the sparse model.…”

Section: Related Workmentioning

confidence: 99%

Globally Variance-Constrained Sparse Representation and Its Application in Image Set Coding

Zhang¹,

Sun²,

Ma³

et al. 2018

IEEE Trans. on Image Process.

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Sparse representation leads to an efficient way to approximately recover a signal by the linear composition of a few bases from a learnt dictionary based on which various successful applications have been achieved. However, in the scenario of data compression, its efficiency and popularity are hindered. It is because of the fact that encoding sparsely distributed coefficients may consume more bits for representing the index of nonzero coefficients. Therefore, introducing an accurate rate constraint in sparse coding and dictionary learning becomes meaningful, which has not been fully exploited in the context of sparse representation. According to the Shannon entropy inequality, the variance of Gaussian distributed data bound its entropy, indicating the actual bitrate can be well estimated by its variance. Hence, a globally variance-constrained sparse representation (GVCSR) model is proposed in this paper, where a variance-constrained rate term is introduced to the optimization process. Specifically, we employ the alternating direction method of multipliers (ADMMs) to solve the non-convex optimization problem for sparse coding and dictionary learning, both of them have shown the state-of-the-art rate-distortion performance for image representation. Furthermore, we investigate the potential of applying the GVCSR algorithm in the practical image set compression, where the optimized dictionary is trained to efficiently represent the images captured in similar scenarios by implicitly utilizing inter-image correlations. Experimental results have demonstrated superior rate-distortion performance against the state-of-the-art methods.

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Section: Related Workmentioning

confidence: 99%

Globally Variance-Constrained Sparse Representation and Its Application in Image Set Coding

Zhang¹,

Sun²,

Ma³

et al. 2018

IEEE Trans. on Image Process.

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show abstract

“…Recently, several researchers adopted the concept of CS-based image compression [22,23,24,25,26,27,28]. As reported by CS theory, the signal must be sparse in transform domain i.e.…”

Section: Cs In Compressionmentioning

confidence: 99%

“…But this scheme suffers from different permutation orders for different vectors. Other CS-based compression techniques were explored in [27,28].…”

Section: Cs In Compressionmentioning

confidence: 99%

Joint Image Watermarking, Compression and Encryption Based on Compressed Sensing and Entropy Coding

Mostafa¹,

Fakhr²

2018

IJMA

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Image usage over the internet becomes more and more important each day. Over 3 billion images are shared each day over the internet which raise a concern about how to protect images copyrights? Or how to utilize image sharing experience? This paper proposes a new robust image watermarking algorithm based on compressed sensing (CS) and quantization index modulation (QIM) watermark embedding. The algorithm capitalizes on the CS to compress and encrypt images jointly with Entropy Coding, Arnold Cat Map, Pseudo-random numbers and Advanced Encryption Standard (AES). Our proposed algorithm works under the JPEG standard umbrella. Watermark embedding is done in 3 different locations inside the image using QIM. Those locations differ with each 8-by-8 image block. Choosing which combination of coefficients to be used in QIM watermark embedding depends on selecting a combination from combinations table, which is generated at the same time with projection matrices using a 10-digits Pseudorandom number secret key SK 1. After quantization phase, the algorithm shuffles image blocks using Arnold's Cat Map with a 10-digits Pseudo-random number secret key SK 2 , followed by a unique method for splitting every 8x8 block into two unequal parts. Part number one will act as the host for two QIM watermarks then goes through encoding phase using Run-Length Encoding (RLE) followed by Huffman Encoding, while part number two goes through sparse watermark embedding followed by a third QIM watermark embedding and compression phase using CS, then Huffman encoder is used to encode this part. The algorithm aims to combine image watermarking, compression and encryption capabilities in one algorithm while balancing how those capabilities works with each other to achieve significant improvement in terms of image watermarking, compression and encryption. 15 different images usually used in image processing benchmarking were used for testing the algorithm capabilities and experiments show that our proposed algorithm achieves robust watermarking jointly with encryption and compression under the JPEG standard framework.

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“…Recently, various sparse algorithms [9][10][11][12] using over-complete dictionaries have been used for image compression. Many strategies have been addressed using sparse approximation algorithms for image compression [13][14][15][16][17][18][19][20][21]. The over complete dictionary based image coding provides a competitive coding gains and visual perceptual quality as compared to popular transform based image coding.…”

Section: Introductionmentioning

confidence: 99%

“…They proposed an algorithm for image compression based on K-SVD algorithm [17]. Yuan [14] presented image compression via sparse reconstruction. Zapeda [18] have presented image coding using iteration tuned and aligned dictionary.…”

Section: Introductionmentioning

confidence: 99%

Dictionary Based Intra Prediction for Image Compression

Sahoo¹,

Das²

2017

Annals of Computer Science and Information Systems

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Abstract-Recently image coding has been important and in many field it is necessary. Recently various sparse algorithms have been developed for image compression. This paper presents a dictionary based block intra prediction for image compression with construction of an adaptive trained dictionary. The adaptive trained dictionary is prepared using K-SVD algorithm. K-SVD algorithm update the dictionary based on the sparse algorithms and given image. The prediction residuals selected from different image are used for dictionary training. The orthogonal matching pursuit (OMP) algorithm have employed for selection of dictionary elements and encoding. The proposed method is then integrated into 9 mode H.264 intra coding. Performance of proposed method compared with existing methods. Simulation result shows that proposed scheme has improved efficiency as compared to existing schemes.

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Image compression via sparse reconstruction

Cited by 7 publications

References 17 publications

Globally Variance-Constrained Sparse Representation and Its Application in Image Set Coding

Globally Variance-Constrained Sparse Representation and Its Application in Image Set Coding

Joint Image Watermarking, Compression and Encryption Based on Compressed Sensing and Entropy Coding

Dictionary Based Intra Prediction for Image Compression

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