On the Perceptual Factors Underlying the Quality of PostCompression Enhancement of Textures

The addition of white noise to an image has been shown to increase the perceived sharpness of the image’s blurred regions under certain conditions. Additive white noise has also been shown to increase the visual quality a compressed image, a finding which has been attributed, in large part, to the noise’s ability to simulate textures that have been lost via the compression. To explore the perceptual underpinnings of this enhancing effect, in this paper, we tested whether the noise can be tuned based on properties of the source texture to provide even greater improvements in quality as compared to white noise. We used a parametric texture-synthesis algorithm to generate statistically and spectrally shaped noise patterns, which were scaled in contrast and then added to corresponding compressed texture regions. Subjects reported both the optimal contrast scaling factors and the associated quality improvement scores relative to the distorted regions. Our results indicate that the addition of the shaped noise can provide markedly greater quality improvements compared to white noise, a finding which cannot be explained by the mere presence of high-frequency content. We discuss how the optimal contrast scalings might be predicted, and we examine the performances of existing quality assessment algorithms on our enhanced images. Introduction Nearly every digital image/video viewed by today’s consumers has undergone some form of lossy compression, and thus contains some form of compression distortions. When bitrate constraints and/or uncorrectable communication errors force these distortions into the visible regime, the only hope for improvement is to attempt to restore or otherwise enhance the decompressed image. To this end, numerous artifact reduction techniques have been proposed to reduce the visibility of blocking artifacts and/or aliasing artifacts [1] and/or attempt to enhance the appearances of degraded edges (e.g., [2]). Even more general image restoration techniques have been proposed, and although these techniques are not specifically designed for compressed images, some have demonstrated the ability to partially reverse the blurring and aliasing caused by compression. Yet, despite all of this previous work, very little effort has focused on improving the visual qualities of the texture regions. During compression, it is most often the textures that are the first to be degraded (usually via blurring), whereas edges tend to remain relatively intact, even under moderate compression. Thus, a technique focused on improving the appearances of the textures could potentially yield substantially greater quality improvements than what is currently possible. Achieving this goal requires a better understanding of the perceptual factors that underlie human judgments of texture quality in an enhancement context. A clever but lesser-known technique of improving the quality of textures in a compressed image is to add a subtle amount of white noise to the image. Kayargadde and Martens were the first to demonstrate that adding noise to a blurred image can improve the perceived sharpness of the image [3]. In [4], Kashibuchi et al. demonstrated that additive white noise can improve the perceived sharpness in printed photographs. In [5], Kurihara et al. investigated the influence of additive noise on the perception of both sine-wave patterns and textures; Kurihara et al. demonstrated that white noise added only to the texture regions consistently yields an increase in the perceived sharpness, though further hypothesis regarding the underlying perceptual factors for this improvement were not explored in [5]. In a follow-up study, Wan et al. investigated how the addition of noise affects the perceived sharpness of various natural textures [6]. Wan et al. proposed that the noise increases the high-frequent content in the image, thus boosting the perceived sharpness. The prevalence of high-frequency content has also been employed by several no-reference image sharpness estimators (e.g., [7]). Wan et al. also demonstrated that the presence of noise facilitates the subject’s ability to recall the texture from memory, thus boosting the perceived sharpness. In the context of compression, in [8], Chandler et al. demonstrated that additive noise can improve the visual quality of images subjected to wavelet-based compression. Chandler et al. proposed that the noise has two additional effects on quality: (1) the noise helps to masks the compression artifacts; and (2) the noise helps to synthesize some of the details lost in the textures. Original Distorted (Compressed) Distorted + White Noise Distorted + (Filtered) Syn. Texture Figure 1. Demonstrative example of the potential of additive, filtered synthesized texture to improve the visual quality of a compressed image to a greater extent than additive white noise. Preprint of paper in Human Vision and Electronic Imaging 2017