Predictive side decoding for human-centered multiple description image coding

• Published in: EURASIP journal on wireless communications and networking Vol. 2020; no. 1; pp. 1 - 14
• Main Author: Xu, Yuanyuan
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 11.05.2020; Springer Nature B.V; SpringerOpen
• Subjects: Communications Engineering; Decoding; Edge; Engineering; Fog; Human-centered computing; Human-centered Computing in Cloud; Image coding; Image enhancement; Information Systems Applications (incl.Internet); Lattice vector quantization; Multiple description coding; Networks; Signal,Image and Speech Processing; Vector quantization; Human-centered computing; Image coding; Multiple description coding; Lattice vector quantization
• ISSN: 1687-1499; 1687-1472; 1687-1499
• DOI: 10.1186/s13638-020-01719-z

Multiple description coding (MDC) provides a favorable solution for human-centered image communication, which takes into account people’s varying watching situations as well as people’s demand for real-time image display. As an effective technique for MDC, three-description lattice vector quantization (3D-LVQ) is considered for image coding in this paper. Based on intra- and inter-correlation in the 3D-LVQ index assignment as well as wavelet intra-subband correlation, a novel predictive decoding method for 3D-LVQ-based image coding is proposed to enhance side decoding performance, which attempts to predict lost descriptions (sublattice points) in a good way for better reconstructions of wavelet vectors (fine lattice points) in the side decoding. Experimental results validate effectiveness of the proposed decoding scheme in terms of rate-distortion performance.