Implementation of Direct Indexing and 2-V Golomb Coding of Lattice Vectors for Image Compression
R. R. Khandelwal1, P. K. Purohit2

1R. R. KHANDELWAL, Associate Professor Department of Electronics Engineering, Shri Ramdeobaba College of Engineering and Management, Katol Road, Nagpur- 440013, Maharashtra, India.
2P. K. PUROHIT, Professor, Department of Science, National Institute of Technical Teachers’ Training & Research, Bhopal, India

Manuscript received on 30 June 2019 | Revised Manuscript received on 05 July 2019 | Manuscript published on 30 July 2019 | PP: 1205-1210 | Volume-8 Issue-9, July 2019 | Retrieval Number: H7443068819/19©BEIESP | DOI: 10.35940/ijitee.H7443.078919
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Abstract: Indexing of code vectors is a most difficult task in lattice vector quantization. In this work we focus on the problem of efficient indexing and coding of indexes. Index assignment to the quantized lattice vectors is computed by direct indexing method, through which a vector can be represented by a scalar quantity which represents the index of that vector. This eliminates the need of calculating the prefix i.e. index of the radius ( R) or norm and suffix i.e. the index of the position of vector on the shell of radius R, also eliminates index assignment to the suffix based on lattice point enumeration or leader’s indexing . Two value golomb coding is used to enumerate indices of quantized lattice vectors. We use analytical means to emphasize the dominance of two value golomb code over one value golomb code. This method is applied to achieve image compression. Indexes of particular subband of test images like barbara, peppers and boat are coded using 2-value golomb coding (2-V GC) and compression ratio is calculated. We demonstrate the effectiveness of the 2-V GC while the input is scanned columnwise as compare to rowwise. Experimentally we also show that good compression ratio is achieved when only higher order bits of the indexes are encoded instead of complete bits.
Keywords: Golomb coding, two value golomb coding, Lattice Vector Quantization (LVQ), indexing, image compression

Scope of the Article: Image Security