High Performance Scalable Image Coding

Nowadays,digital image has found wide applications in many areas.The vastamount of data imposes unprecedented pressure on storage capacity,transmissionbandwidth and computer processing speed.To find efficient ways to organize,store,transmit and restore image data or develop coding methods which enjoy the propertiesof high compression ratio,good quality and low complexity,is one of the key tasks ofmodern signal processing.With the popularity of Internet and the emergence of diversemultimedia applications,great challenges have been presented to traditional methods.Besides the need of high compression efficiency,image coding is also required to adaptto heterogeneous environments,including different demands from users,varioustransmission conditions as well as different receiver capacities.Aiming at developing high performance image coding methods which meet theabove requirements,this dissertation investigates major issues of image coding,including transform,quantization and coding.The main results are as follows:1.The embedded wavelet image coding techniques are studied in detail.Theadvanced organization and coding methods of wavelet coefficients are classified anddiscussed.2.The principle and methods of set partitioning are investigated.An adaptive setpartitioning algorithm is proposed.The structure of spatial orientation tree is extendedand several trees are joined together to represent one insignificant set,resulting in bitsavings in set representation.3.The application of vector quantization to embedded image coding is investigated.A coding scheme which combines the algorithm of set partitioning in hierarchical tree(SPIHT) and lattice vector quantization is proposed.In this scheme,regular lattices arechosen as the codebook,no training or storage of the codebook or significant encodingcomputations is required and thus the high complexity problem of traditional vectorquantization is overcome.The multistage gain-shape vector quantization is modifiedand incorporated into SPIHT system,which provides better coding performances forimages with more details. 4.By combining several advanced techniques,an efficient embedded waveletcoding algorithm is proposed.It employs morphological representation,quadtreepartitioning as well as efficient context-based adaptive coding to jointly exploit bothwithin-subband clustering and cross-subband similarity of wavelet coefficients.Guidedby the embedding principle,a cluster-based classification and sorting strategy isproposed.Based on the list structure,a fine fractional bit-plane coding is achieved withrelatively low complexity.Experimental results show that the proposed algorithmoutperforms the state-of-the-art coders for both lossy and lossless compression.Inaddition,a highly flexible image codec is proposed based on above algorithm.Itexploits inherent multi-resolution and multi-precision nature of subband bitplane codingto achieve both resolution and rate scalability.Importantly,the encoding can beindependently conducted without knowledge of the final decoding situation.Ahierarchical codestream structure is established and it can be easily parsed and reorderedto meet the different requirements of the users.5.The problem of sparse approximation is studied and fast methods of sparseexpansion over the redundant geometric dictionary are investigated.The popularmatching pursuit (MP) algorithm and its properties are analyzed thoroughly.A novelidea of estimating the cross-correlation between dictionary atoms is introduced and aseries of fast MP algorithms are proposed.Experimental results show that the proposedalgorithms offer important complexity reductions while maintaining the highapproximation performances.For instance,compared with the latest full searchalgorithm,a speed-up gain up to 115.39 times is achieved for 512×512 test images withan average PSNR loss of 0.25 dB.6.A geometric image coding scheme based on fast MP algorithms is designed.Thedistributions of selected atoms and coefficients resulting from image decomposition arestudied.A block partitioning coding method is proposed to jointly code the atompositions and coefficient magnitudes.The proposed method has striking advantagesover the latest MP coder in computational complexity,coding efficiency as well asscalability.For instance,for 512×512 test images an average PSNR gain of 1.75 dB isachieved.Notably,thanks to the geometrical structure of the dictionary,the new coderprovides interesting adaptability features which allow the codestream to be easily andefficiently decoded at any spatial resolution.This makes it very attractive for various imaging applications over heterogeneous networks.