A Methodology for Extracting Power-Efficient and Contrast Enhanced RGB Images

Abstract

Smart devices have become an integral part of people's lives. The most common activities for users of such smart devices that are energy sources are voice calls, text messages (SMS) or email, browsing the World Wide Web, streaming audio/video, and using sensor devices such as cameras, GPS, Wifi, 4G/5G, and Bluetooth either for entertainment or for the convenience of everyday life. In addition, other power sources are the device screen, RAM, and CPU. The need for communication, entertainment, and computing makes the optimal management of the power consumption of these devices crucial and necessary. In this paper, we employ a computationally efficient linear mapping algorithm known as Concurrent Brightness & Contrast Scaling (CBCS), which transforms the initial intensity value of the pixels in the YC_bC_r color system. We introduce a methodology that gives the user the opportunity to select a picture and modify it using the suggested algorithm in order to make it more energy-friendly with or without the application of a histogram equalization (HE). The experimental results verify the efficacy of the presented methodology through various metrics from the field of digital image processing that contribute to the choice of the optimal values for the parameters a,b that meet the user's preferences (low or high-contrast images) and green power. For both low-contrast and low-power images, the histogram equalization should be omitted, and the appropriate a should be much lower than one. To create high-contrast and low-power images, the application of HE is essential. Finally, quantitative and qualitative evaluations have shown that the proposed approach can achieve remarkable performance.

Keywords: color system; histogram equalization; power consumption; smart devices.

Figure 1

Block diagram of the proposed approach.

Figure 2

Data for evaluation: Lena, Car, and Baboon RGB images.

Figure 3

$PRR$ and $SSIM$ of the ${\mathbf{Y}}_{CBCS}$ (without HE) and ${\mathbf{Y}}_{new}$ (${\mathbf{Y}}_{CBCS}$ with HE) for Lena image.

Figure 4

$PSNR$ and $MSE$ of the ${\mathbf{Y}}_{CBCS}$ (without HE) and ${\mathbf{Y}}_{new}$ (${\mathbf{Y}}_{CBCS}$ with HE) for Lena image.

Figure 5

Resulted image ${\mathbf{I}}_{new}$ using ${\mathbf{Y}}_{CBCS}$ (without HE) for $a=0.5$ and $b$ = [10:10:30].

Figure 6

Resulted image ${\mathbf{I}}_{new}$ using ${\mathbf{Y}}_{CBCS}$ (without HE) for $a=0.7$ and $b$ = [10:10:30].

Figure 7

Lena resulted image ${\mathbf{I}}_{new}$ for $a=\{0.5,0.7\}$, b = [80:10:100].

Figure 8

Lena resulted image ${\mathbf{I}}_{new}$ for $a=1$ and b = [20:10:40].

Figure 9

Baboon resulted image ${\mathbf{I}}_{new}$ using ${\mathbf{Y}}_{CBCS}$ (without HE) for $a=0.5$ and b = [10:10:30].

Figure 10

Baboon resulted image ${\mathbf{I}}_{new}$ using ${\mathbf{Y}}_{CBCS}$ (without HE) for $a=0.7$ and b = [10:10:30].

Figure 11

Car resulted image ${\mathbf{I}}_{new}$ using ${\mathbf{Y}}_{CBCS}$ (without HE) for $a=0.5$ and b = [10:10:30].

Figure 12

Car resulted image ${\mathbf{I}}_{new}$ using ${\mathbf{Y}}_{CBCS}$ (without HE) for $a=0.7$ and b = [10:10:30].

Figure 13

Baboon resulted image ${\mathbf{I}}_{new}$ using ${\mathbf{Y}}_{CBCS}$ (with HE) for $a=1$ and b = [10:10:30].

Figure 14

Car resulted image ${\mathbf{I}}_{new}$ using ${\mathbf{Y}}_{CBCS}$ (with HE) for $a=1$ and b = [10:10:30].