Abstract: Histogram Equalization (HE) technique is simple and effectively used for contrast enhancement. But HE is not suitable for consumer electronic products because it may produces washed out appearance image. The new quantitative measures of image enhancement and frequency domain based methods used for object detection and visualization and enhancement technique driven by both global and local process on luminance and chrominance components of the image. This measure of image enhancement is related with the concepts of Weber's law of the human visual systems. It helps to choose the best parameters and transform. This approach based on parameter controlled histogram distribution method can enhance simultaneously overall contrast and sharpness of an image. This approach also increases the visibility of specified portions or better maintaining image color. This analysis provides better performance for contrast enhancement Index terms: Histogram equalization, Image enhancement, Contrast enhancement, I.INTRODUCTION A digital image is converted into analogy signal which is scanned into a display. Before the processing image is converted into digital format, Digitization includes sampling of image and quantization of sampled values. After converting the image into bit, information is processed. In image processing, images are available in digitized form that is arrays of finite length binary format. For digitization, the given images are sampled on a discrete grid and each sample or pixel is quantized using a finite number of bits. The digitized image is used in computer. Image enhancement which transforms digital images to enhance the visual information. To enhance image contrast is the intensity mapping that reassigns the intensity of pixels through a monotonically increasing function. Primary operation for all vision and image processing tasks in several areas. In forensic video/image analysis tasks surveillance videos have quite different qualities compared with other videos such as the videos for high quality entertainment or TV broadcasting. Enhancement transformation to modify the contrast of an image within a display's dynamic range is therefore required in order to show full information content in the videos. Contrast enhancement is an important function in image processing applications. The objective of this method is to make an image clearly recognized for a specific application. Point operation based enhancement techniques are contrast stretching, non-linear point transformation, histogram modeling. The non-linearity is introduced by many imaging lighting device which can be described with a point operation. Gamma correction is using the power law's light intensity operation [1] which is adjusting the lightness/darkness level of their prints. X=(x) (1/gamma value) Where x is the original pixel value. Depending on the gamma value, image can be lightened or darkened. so, it will improve visual contrast and also decreases the visual contrast too. Histogram Equalization (HE) is most popular technique for contrast enhancement. HE makes uniform distribution of the gray level for an image. But, consumer electronics such as Flat panel display (FPD), HE is rarely applied in directly because the significant changes in brightness [2]. The HE effectiveness is depends on the contrast of the original image. In general, HE will flatten out the probability distribution of an image and increase its dynamic range and also will make the average brightness towards the middle gray level of an image regardless of the input image, and produce the objectionable artifacts and unnatural contrast effect. This makes the visual quality of processed image is unsatisfactory. Surveillance videos have quietly different qualities compared with other videos such as videos for high quality entertainment or T
