Analyzing the Effect of JPEG Compression on Local Variance of Image Intensity

Abstract

The local variance of image intensity is a typical measure of image smoothness. It has been extensively used, for example, to measure the visual saliency or to adjust the filtering strength in image processing and analysis. However, to the best of our knowledge, no analytical work has been reported about the effect of JPEG compression on image local variance. In this paper, a theoretical analysis on the variation of local variance caused by JPEG compression is presented. First, the expectation of intensity variance of 8×8 non-overlapping blocks in a JPEG image is derived. The expectation is determined by the Laplacian parameters of the discrete cosine transform coefficient distributions of the original image and the quantization step sizes used in the JPEG compression. Second, some interesting properties that describe the behavior of the local variance under different degrees of JPEG compression are discussed. Finally, both the simulation and the experiments are performed to verify our derivation and discussion. The theoretical analysis presented in this paper provides some new insights into the behavior of local variance under JPEG compression. Moreover, it has the potential to be used in some areas of image processing and analysis, such as image enhancement, image quality assessment, and image filtering.

Saliency enabled compression in JPEG framework

“…When any DWT or DCT methods in [6, 10–12, 14] are applied on the most important regions , the mean‐square error (MSE) of these regions is higher than overall MSE. The reason for this is while applying these transform‐based methods in a region with high variance, the energy compaction is lesser compared to a region with lower variance [18], which results in higher MSE after quantisation to achieve lower bit‐rate. A similar example can be referred from rate‐distortion curve in Fig.…”

“…a matrix of quantisation step sizes). According to JPEG standard, the quantisation table can be configured as per the bit‐rate requirement [18]. There have been series of several quantisation tables developed and are widely used for the requirement of higher CR or improved reconstructed image quality.…”

“…We observed that the reconstructed blocks with higher variance (i.e. the second central moment of the pixel intensities within a block) are highly degraded, compared to the blocks with lower variance [18, 19]. This happens due to the structure of JPEG baseline quantisation table designed to quantise higher frequency regions more heavily than the lower frequency ones.…”

Social Network Identification Through Image Classification With CNN

“…In this subsection, we outline some interesting observations that motivate the use of noise residual features. In particular, we want to analyze how the up/downloading of an image on a social network affects the local variance of noise residual following the idea of the paper in [28], where the variance is measured on image intensity to deal with JPEG compression. To this end, we consider images of the UCID social dataset described in the previous subsection and for each observed image, we extract the noise residual according to Equation (1).…”

Greedy Algorithm for Image Compression in Image Processing

“…Jianquan Yang, et.al (2015) proposed in this paper [11], a hypothetical examination on the variety of local variance brought about by JPEG compression is introduced. To start with, the desire of intensity variance of 8×8 non-covering blocks in a JPEG image is inferred.…”