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Articles 1 - 6 of 6
Full-Text Articles in Physical Sciences and Mathematics
Index-Compressed Vector Quantisation Based On Index Mapping, Jamshid Shanbehzadeh, Philip Ogunbona
Index-Compressed Vector Quantisation Based On Index Mapping, Jamshid Shanbehzadeh, Philip Ogunbona
Professor Philip Ogunbona
The authors introduce a novel coding technique which significantly improves the performance of the traditional vector quantisation (VQ) schemes at low bit rates. High interblock correlation in natural images results in a high probability that neighbouring image blocks are mapped to small subsets of the VQ codebook, which contains highly correlated codevectors. If, instead of the whole VQ codebook, a small subset is considered for the purpose of encoding neighbouring blocks, it is possible to improve the performance of traditional VQ schemes significantly. The performance improvement obtained with the new method is about 3dB on average when compared with traditional …
Channel-Optimized Vector Trellis Source Coding For The Awgn Channel, Philip Secker, Philip Ogunbona
Channel-Optimized Vector Trellis Source Coding For The Awgn Channel, Philip Secker, Philip Ogunbona
Professor Philip Ogunbona
A channel-optimised (joint source and channel) trellis source coder is designed for the AWGN channel. The optimum decoder is a non-linear function of the real channel information. The extension to 2D vector alphabets coupled with modifications to the signal space are found to improve performance. Favourable comparisons are made against a trellis source coder/TCM system.
Index Factorised Image Adaptive Vector Quantisation, Jamshid Shanbehzadeh, Philip Ogunbona
Index Factorised Image Adaptive Vector Quantisation, Jamshid Shanbehzadeh, Philip Ogunbona
Professor Philip Ogunbona
No abstract provided.
Index Compressed Tree-Structured Vector Quantisation, Jamshid Shanbehzadeh, Philip Ogunbona
Index Compressed Tree-Structured Vector Quantisation, Jamshid Shanbehzadeh, Philip Ogunbona
Professor Philip Ogunbona
This paper introduces a novel coding scheme based on Tree-Structured Vector Quantisation (TSVQ) scheme for image compression. The genealogical relationship among the indices of the neighbouring blocks generated from vector quantisation is exploited to improve the coding performance of TSVQ. The proposed coding scheme provides about 3.5 dB improvement over the basic TSVQ scheme and outperforms VQ schemes with memory and JPEG coding standard at low bit-rates. In addition its performance is comparable with address VQ but with much less complexity.
Index Compressed Image Adaptive Vector Quantisation, Jamshid Shanbehzadeh, Philip Ogunbona
Index Compressed Image Adaptive Vector Quantisation, Jamshid Shanbehzadeh, Philip Ogunbona
Professor Philip Ogunbona
This paper introduces an improved image adaptive vector quantisation technique - index compressed image adaptive vector quantisation (IC-IAVQ). Despite its advantage over the universal codebook VQ, basic image adaptive VQ (IAVQ) is still suboptimum; it neglects the correlation among block indices in the encoded image. The new technique, IC-IAVQ, overcomes this suboptimality through a pre-processing and lossless compression of block indices. Simulation results using several images show that IC-IAVQ outperforms IAVQ and entropy coded IAVQ, especially at low bit-rates by about 2dB on average.
New Feature-Based Image Adaptive Vector Quantisation Coder, Jamshid Shanbehzadeh, Philip O. Ogunbona
New Feature-Based Image Adaptive Vector Quantisation Coder, Jamshid Shanbehzadeh, Philip O. Ogunbona
Professor Philip Ogunbona
It is difficult to achieve a good low bit rate image compression performance with traditional block coding schemes such as transform coding and vector quantization, without regard for the human visual perception or signal dependency. These classical block coding schemes are based on minimizing the MSE at a certain rate. This procedure results in more bits being allocated to areas which may not be visually important and the resulting quantization noise manifests as a blocking artifact. Blocking artifacts are known to be psychologically more annoying than white noise when the human visual response is considered. While image adaptive vector quantization …