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Full-Text Articles in Physical Sciences and Mathematics
Image Similarity Index Based On Moment Invariants Of Approximation Level Of Discrete Wavelet Transform, Prashan Premaratne, Malin Premaratne
Image Similarity Index Based On Moment Invariants Of Approximation Level Of Discrete Wavelet Transform, Prashan Premaratne, Malin Premaratne
Dr Prashan Premaratne
Subjective quality measures based on the human visual system for images do not agree well with well-known metrics such as mean squared error and peak signal-to-noise ratio. Recently, the structural similarity measure (SSIM) has received acclaim owing to its ability to produce results on a par with the human visual system. However, experimental results indicate that noise and blur seriously degrade the performance of the SSIM metric. Furthermore, despite the SSIM's popularity, it does not provide adequate insight into how it handles the 'structural similarity' of images. Proposed is a new structural similarity measure based on the approximation level of …
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.