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-The storage and transmission of imagery become more challenging task in the current scenario of multimedia applications. Hence, an efficient compression scheme is highly essential for imagery, which reduces the requirement of storage medium and transmission bandwidth. Not only improvement in performance and also the compression techniques must converge quickly in order to apply them for real time applications. There are various algorithms have been done in image compression, but everyone has its own pros and cons. Here, an extensive analysis between existing methods is performed. Also, the use of existing works is highlighted, for developing the novel techniques which face the challenging task of image storage and transmission in multimedia applications.

A performance analysis of conventional Convolutional Neural Network (CNN) based denoising method is proposed. In this image denoising method, the contrast of images is adaptively enhanced. Generally, it is not possible to capture the imageswith good quality for all situations. Because they are captured in various light conditions.So, the captured images are suffered by noise, which results in poor perceived image quality. Thus, it is necessary to improve the quality of images with edge detail preservation as much as possible. The convolutional neural network model for low light image enhancement is already developed and is named as DnCNNs. Here, the performance analysis of image denoising using the DnCNNmodel is presented. The DnCNN implicitly removes the noise in the image. The simulation results afford better reference for application developers.

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