CNN-Based Suppression of False Contour and Color Distortion in Bit-Depth Enhancement

Abstract

It is a challenge to transmit and store the massive visual data generated in the Visual Internet of Things (VIoT), so the compression of the visual data is of great significance to VIoT. Compressing bit-depth of images is very cost-effective to reduce the large volume of visual data. However, compressing the bit-depth will introduce false contour, and color distortion would occur in the reconstructed image. False contour and color distortion suppression become critical issues of the bit-depth enhancement in VIoT. To solve these problems, a Bit-depth Enhancement method with AUTO-encoder-like structure (BE-AUTO) is proposed in this paper. Based on the convolution-combined-with-deconvolution codec and global skip of BE-AUTO, this method can effectively suppress false contour and color distortion, thus achieving the state-of-the-art objective metric and visual quality in the reconstructed images, making it more suitable for bit-depth enhancement in VIoT.

Optimization of Plane Image Color Enhancement Processing Based on Computer Vision Virtual Reality

“…Image enhancement technology, one of the important parts of digital image processing, has been widely used. Traditional image enhancement techniques mainly include spatial processing and frequency domain processing [9]. e spatial processing method is to directly process the pixels of the image, which mainly includes the histogram equalization method, specified return method, gray transformation method, and Retinex method.…”

A Computer Vision System for Staff Gauge in River Flood Monitoring

“…V-IoT systems produce huge amounts of data in relatively short time, forcing to be mindful in managing them. In order to mitigate the transmission and storage constraints, Peng et al [12] proposed an enhancement method to overcome the false contour and color distortion connected with bit-depth compression, given that this is a widely used solution. As a general suggestion, Ji et al [13] highlighted the importance of optimising transmission, storage and processing, based on the specific system tasks to be completed, taking into consideration delay sensitive and context aware video services.…”