Wavelet-based foveated image quality measurement for region of interest image coding

Abstract

Region of interest (ROI) image and video compression techniques have been widely used in visual communication applications in an effort to deliver good quality images and videos at limited bandwidths. Most image quality metrics have been developed for uniform resolution images. These metrics are not appropriate for the assessment of ROI coded images, where space-variant resolution is necessary. The spatial resolution of the human visual system (HVS) is highest around the point of fixation and decreases rapidly with increasing eccentricity. Since the ROIs are usually the regions "fixated" by human eyes, the foveation property of the HVS supplies a natural approach for guiding the design of ROI image quality measurement algorithms. We have developed an objective quality metric for ROI coded images in the wavelet transform domain. This metric can serve to mediate the compression and enhancement of ROI coded images and videos. We show its effectiveness by applying it to an embedded foveated image coding system.

Foveation scalable video coding with automatic fixation selection

“…Most image quality measurement methods in the literature are not appropriate because they are designed for uniform resolution images. In [20], [60], a wavelet-based foveated image quality assessment metric called Foveated Wavelet Quality Index (FWQI) was proposed by combining the wavelet domain visual sensitivity model (17) and a novel image quality indexing algorithm [61], [62]. FWQI has a dynamic range of [0,1], where 1 represents the best quality.…”

Foveation image coding with fuzzy-based joint parameter selection

“…In contrast, image contents in the unimportant regions can be coarsely represented to save coding bits. The contents of images can be represented in the domain of DWT coefficients [24,27,28], DCT coefficients [9], or pixels. In this paper, we represent image contents in the DWT domain because DWT coefficients have the embedded properties, which are suitable for scalable storage and transmission [25].…”

“…Note that QP is used to reduce a dynamic range of wavelet coefficients reflecting the coarseness level representation of wavelet coefficients. The objective is to achieve the best image quality in terms of foveated wavelet image quality index (FWQI) [28]. Fig.…”

“…Arnow and Bovik [1] applied the foveation concept to the corner search problems. In [27,28], Wang et al proposed the embedded foveation image coding (EFIC) algorithm, which orders the encoded bitstream to optimize foveated visual quality at arbitrary bit rate. In the EFIC algorithm, the discrete wavelet coefficients obtained from transforming the original image are weighted and are directly encoded with the modified SPIHT codec [25], in which the scanned order of wavelet coefficients are rearranged.…”

“…The foveation image/video processing is one of the most important techniques in exploiting the HVS system to achieve efficient image/video coding [2,6,7,[9][10][11]16,22,[26][27][28]30]. The foveation image/video processing is based on the fact that the spatial resolution of the HVS is highest around the foveation points and decreases rapidly with increasing eccentricity.…”