Low Complexity Gray-scale Image Compression Method Using Index-Based Approach.pptx

Low Complexity Gray-scale
Image Compression Method
Using Index-Based Approach

About the paper
 Published in 2021
 No of citations: 11
 Published in Asian Conference on Innovation in Technology

Contents
 Introduction
 Problem Statement
 Related work
 Proposed Approach
 Results
 Conclusion

Introduction
 Data compression is essential for minimizing redundant data and optimizing
transmission bandwidth and storage space.
 Image compression aims to encode images using fewer bits while maintaining
visual quality, facilitating efficient transmission and storage.
 Various compression methods have been developed to achieve better
compression ratios while balancing computational complexity.
 Compression algorithms are categorized into lossless and lossy methods, with
lossy compression sacrificing some image quality for higher compression ratios.

Problem Statement
 The known state of the art methods requires extensive computational resources
These methods contains complex algorithms that are difficult to implement.
 Most of the research work is focused on different methods based on discrete
cosine transform, singular value decomposition, and wavelets. No work has been
carried out using the position of pixels.

Related Work
 Development of a novel image compression scheme for encrypted grayscale
images, exploiting quantization and spatial correlation during compression and
context-adaptive interpolation during decompression
 Known image compression algorithm utilizing bit plane slicing, achieving better
compression ratios and reconstructed image quality through mean, variance, and
standard deviation analysis
 Block-based image segmentation algorithm utilizing conditional probability
within a Bayesian framework for classifying image blocks into edge, monotone,
and textured blocks, suitable for object-based image compression

Proposed Solution
 This approach makes use of simple indices of an array to compress images. The
context of nearby fifteen pixels is used, and the index for the current pixel is found.
 The indices are found for every pixel of an image. These indices are transformed
and written into a transformed file.
 This transformed file is further compressed with Huffman compression to achieve a
compressed file.

Image size before and after compression, compression ratio,
PSNR for proposed methodology

Low Complexity Gray-scale Image Compression Method Using Index-Based Approach.pptx

Conclusion
 The proposed image compression algorithm is simple and has less complexity,
and produces high fidelity images at low PSNR values.
 The compression ratio achieved is more than two. The reconstructed images
quality from the proposed algorithm is good.
 At same PSNR values, the reconstructed image quality by the proposed
algorithm is better than quality of JPEG, which is justified by the computation of
accurately reconstructed number of pixels

Low Complexity Gray-scale Image Compression Method Using Index-Based Approach.pptx

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