Prediction error preprocessing for perceptual color image compression

Abstract

In this article, a prediction error preprocessor based on the just noticeable distortion (JND) for the color image compression scheme is presented. The dynamic range of prediction error signals we can reduce, the lower bit rate of the reconstructed image we can obtain at high visual quality. We propose a color JND estimator that is incorporated into the design of the preprocessor in the compression scheme. The color JND estimator is carried out in the wavelet domain to present good estimates to the available amount masking. The estimated JND is used to preprocess the signal and is also used to incorporate into the design of the quantization stage in the compression scheme for higher performance. Simulation results show that the bit rate required by the compression scheme with the preprocessor is lower at high visual quality of the reconstructed color image. The preprocessor is further applied to the input color image of the JPEG and JPEG2000 coders for better performance.

Fractional-order 4D hyperchaotic memristive system and application in color image encryption

“…parts, chaotic system initial values(x 0 ,y 0 ,z 0 ,w 0 ), parameters a, b, α, β, γ, q, ρ, cycle numbers m, n, starting acid base c 0 (c 0 ∈A, T, C, G) and DNA encoding rules in Table 1 α1, β1(α1, β1∈ [1,8]).…”

“…Nowadays, digital image is an improtant carrier of information, because of the inherent performance of digital images, including bulk data capacity, high redundancy and extremely strong correlation in adjacent pixels, which make digital image processing a research hotspot. For example, prediction error preprocessing for image compression [1], histogram equalization of images [2], image compression and reconstruction [3], and so on. To achieve the requirement of digital image safety transmission, researchers are interested in an encryption algorithm based on a chaotic system.…”

“…Up to now, all kinds of image encryption algorithms through chaotic system are proposed [1,[8][9][10][11][12][13]. For example, Hua et al [8] proposed an image encryption scheme using 2D Logistic-adjusted-Sine map.…”

A unified framework for spatiotemporal salient region detection

“…The pixels with higher intensity values in saliency map denote that the corresponding pixels are visually important. The saliency map can be used for applications, such as object-ofattention segmentation [2][3][4], object detection [5,6], image and video summarization [7], video surveillance [8], and image and video compression [9].…”