On Image Compression using Curve Fitting
Independent thesis Advanced level (degree of Master (Two Years))Student thesis
Context: Uncompressed Images contain redundancy of image data which can be reduced by image compression in order to store or transmit image data in an economic way. There are many techniques being used for this purpose but the rapid growth in digital media requires more research to make more efficient use of resources. Objectives: In this study we implement Polynomial curve fitting using 1st and 2nd curve orders with non-overlapping 4x4 and 8x8 block sizes. We investigate a selective quantization where each parameter is assigned a priority. The 1st parameter is assigned high priority compared to the other parameters. At the end Huffman coding is applied. Three standard grayscale images of LENA, PEPPER and BOAT are used in our experiment. Methods: We did a literature review, where we selected articles from known libraries i.e. IEEE, ACM Digital Library, ScienceDirect and SpringerLink etc. We have also performed two experiments, one experiment with 1st curve order using 4x4 and 8x8 block sizes and second experiment with 2nd curve order using same block sizes. Results: A comparison using 4x4 and 8x8 block sizes at 1st and 2nd curve orders shows that there is a large difference in compression ratio for the same value of Mean Square Error. Using 4x4 block size gives better quality of an image as compare to 8x8 block size at same curve order but less compression. JPEG gives higher value of PSNR at low and high compression. Conclusions: A selective quantization is good idea to use to get better subjective quality of an image. A comparison shows that to get good compression ratio, 8x8 block size at 1st curve order should be used but for good objective and subjective quality of an image 4x4 block size at 2nd order should be used. JPEG involves a lot of research and it outperforms in PSNR and CR as compare to our proposed scheme at low and high compression ratio. Our proposed scheme gives comparable objective quality (PSNR) of an image at high compression ratio as compare to the previous curve fitting techniques implemented by Salah and Ameer but we are unable to achieve subjective quality of an image.
Place, publisher, year, edition, pages
2010. , 43 p.
Image compression, Multiplication limited and division free, Surface fitting
IdentifiersURN: urn:nbn:se:bth-3144Local ID: oai:bth.se:arkivexED50D979491A14DDC1257831006F01F9OAI: oai:DiVA.org:bth-3144DiVA: diva2:830444
Lundberg, Professor Lars