Quantization and Entropy Coding Scheme for Dictionary Learning Based Image Compression

Most recently, there has been a growing interest in the study of dictionary learning based (DL-based) image compression, which has potential in relieving the bandwith-hungry bottleneck of visual communication. All existing DL-based image compression approaches mainly focus on the effective represent...

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Veröffentlicht in:2016 IEEE 84th Vehicular Technology Conference (VTC-Fall) S. 1 - 5
Hauptverfasser: Wang, Juan, Tao, Xiaoming, Liu, Xijia, Ge, Ning, Lu, Jianhua
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.09.2016
Schlagworte:
Approximation algorithms
Channel coding
Dictionaries
Entropy coding
Image coding
Image reconstruction
Quantization (signal)
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Zusammenfassung:Most recently, there has been a growing interest in the study of dictionary learning based (DL-based) image compression, which has potential in relieving the bandwith-hungry bottleneck of visual communication. All existing DL-based image compression approaches mainly focus on the effective representation of images, thus losing sight of two basic elements of image compression, i.e., quantization and entropy coding. For this reason, this paper proposes a quantization and entropy coding scheme for DL-based image compression. In our scheme, the proposed Partition-Interval K-means (PIK) quantizer adaptively maps continuous coefficients to discrete values. The arithmetic coding, combined with differential coding technique, is applied to encode the indices of nonzero coefficients as well as the labels of quantization values. In our experiments, the proposed scheme is verified to be more effective than other quantization and entropy coding schemes for DL-based image compression.
DOI:10.1109/VTCFall.2016.7881185