Glaucoma is a chronic eye disease that leads to vision loss Glaucoma is caused due to the increase in intraocular pressure of the eye. The intraocular pressure increases due to malfunction or malformation of the drainage system of the eye. A Dual Tree Complex Wavelet Transform (DTCWT) based image compression is proposed to solve factors such as low image contrast, speckle noise, high spatial resolution. DTCWT is used to compress the OCT, SD-OCT and secondary images and it is efficient for detecting glaucoma compared with the existing detection algorithms. Then the compression of OCT, SD-OCT and secondary images are carried out by 2D-oriented Biorthogonal Wavelet Transform (2D-BWT). In this BWT, effective coding performance is achieved by the Windowed All Phase Digital Filter (WAPDF) based on Discrete Cosine Transform (DCT). In BWT, Context-adaptive binary arithmetic coding (CABAC) with Lattice Vector Quantization (LVQ) is presented to encode the wavelet significant coefficients for finding the compressed image. The experimental result has demonstrated the significant improvement of the 2D-BWT with WAPDF gain for OCT, SD-OCT and secondary images with high resolution. Then apply Quincunx mexican-hat-like Wavelet Transform (QWT) with modified Support Vector Clustering (mSVC) proposed to evaluate the OCT, SD-OCT and secondary images. It is used to improve the image quality and reduced the image size for storage, an efficient image compression scheme has been presented by using transform and clustering schemes. The result proves that proposed QWT with mSVC system provides higher CR, correlation, PSNR, SSIM and lower MSE, execution time for OCT, SD-OCT and secondary images significantly.