Vector quantization and signal compression
Vector quantization and signal compression
Arithmetic coding for data compression
Communications of the ACM
Information hiding based on search-order coding for VQ indices
Pattern Recognition Letters
Finite-state vector quantization for waveform coding
IEEE Transactions on Information Theory
An index coding algorithm for image vector quantization
IEEE Transactions on Consumer Electronics
Next-state functions for finite-state vector quantization
IEEE Transactions on Image Processing
Image compression using finite-state vector quantization with derailment compensation
IEEE Transactions on Circuits and Systems for Video Technology
Huffman-code strategies to improve MFCVQ-based reversible data hiding for VQ indexes
Journal of Systems and Software
Comparison of Forbidden Zone Data Hiding and Quantization Index Modulation
Digital Signal Processing
A novel VQ-based reversible data hiding scheme by using hybrid encoding strategies
Journal of Systems and Software
Rough clustering using generalized fuzzy clustering algorithm
Pattern Recognition
Information Sciences: an International Journal
Low bit-rate information hiding method based on search-order-coding technique
Journal of Systems and Software
A high-performance reversible data-hiding scheme for LZW codes
Journal of Systems and Software
Reversibility of image with balanced fidelity and capacity upon pixels differencing expansion
The Journal of Supercomputing
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For the compression of memoryless vector quantization (VQ), most of the lossless index coding algorithms are not suitable for various test images. As a result, we present a hybrid dynamic tree-coding scheme (DTCS) and modified search order coding scheme (MSOC) to re-encode the output index map efficiently without causing any extra coding distortion. The main idea behind this scheme is that the adjacent left and upper around the current processed block usually provide more useful information than its adjacent left-upper and right-upper block, thus we employ two different coding methods according to their corresponding left or upper spatial relations. In addition, we applied the HLIC method to the information hiding. The proposed method does not modify the contents of the secret data and the compressed image. Experimental results show that the newly proposed algorithm achieves significant reduction of bit rate compared to the other lossless index coding schemes for various test images and different codebook sizes. The proposed information hiding scheme can hide a huge amount of information in the index map of an image and allows complete reconstruction of the indexes of the image.