JPEG2000 Still Image Coding Quality

Abstract

This work demonstrates the image qualities between two popular JPEG2000 programs. Two medical image compression algorithms are both coded using JPEG2000, but they are different regarding the interface, convenience, speed of computation, and their characteristic options influenced by the encoder, quantization, tiling, etc. The differences in image quality and compression ratio are also affected by the modality and compression algorithm implementation. Do they provide the same quality? The qualities of compressed medical images from two image compression programs named Apollo and JJ2000 were evaluated extensively using objective metrics. These algorithms were applied to three medical image modalities at various compression ratios ranging from 10:1 to 100:1. Following that, the quality of the reconstructed images was evaluated using five objective metrics. The Spearman rank correlation coefficients were measured under every metric in the two programs. We found that JJ2000 and Apollo exhibited indistinguishable image quality for all images evaluated using the above five metrics (r>0.98, p<0.001). It can be concluded that the image quality of the JJ2000 and Apollo algorithms is statistically equivalent for medical image compression.

Tensor Decomposition Learning for Compression of Multidimensional Signals

“…Well known examples include Fourier transform, Karhunen-Loeve Transform (KLT), Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT). Typical transformation methods for the compression of multispectral images apply DWT [18]- [21], KLT [22], [23], or Principal Components Analysis (PCA) [24] for introducing spectral decorrelation, followed by JPEG2000 [25], [26] for decorrelating the spatial information and performing the quantization stage and the entropy coding.…”

Image quality assessment by an efficient correlation‐based metric

“…Another method of measuring image quality is based on the assumption that human vision of an image should be a block rather than a point. For example, Wang et al 11 and Chen et al [18][19][20][21] measured the correlations between windows as the basis for image quality indices. This type of CM metric is introduced below.…”

Lossy Compression of Biometric Images Implemented Using Floating Point DSP Processor

“…Spatial and spectral redundancies are the two types of redundancies that ensue in images. The correlation that joins neighbouring pixels together causes spatial redundancy while spectral redundancy occur according to the correlation relating different color planes [3]. In the compression theory both spatial and spectral redundancies can be eliminated by utilizing transform coding or subband coding (Discrete Cosine Transform).…”