In recent years, it has been evident that internet is the most effective means of transmitting information in the form of documents, photographs, or videos around the world. The purpose of an image compression method is to encode a picture with fewer bits while retaining the decompressed image's visual quality. During transmission, this massive data necessitates a lot of channel space. In order to overcome this problem, an effective visual compression approach is required to resize this large amount of data. This work is based on lossy image compression and is offered for static color images. The quantization procedure determines the compressed data quality characteristics. The images are converted from RGB to International Commission on Illumination CIE La * b * ; and YCbCr color spaces before being used. In the transform domain, the color planes are encoded using the proposed quantization matrix. To improve the efficiency and quality of the compressed image, the standard quantization matrix is updated with the respective image block. We used seven discrete orthogonal transforms, including five variations of the Complex Hadamard Transform, Discrete Fourier Transform and Discrete Cosine Transform, as well as thresholding, quantization, de-quantization and inverse discrete orthogonal transforms with CIE La * b * ; and YCbCr to RGB conversion. Peak to signal noise ratio, signal to noise ratio, picture similarity index and compression ratio are all used to assess the quality of compressed images. With the relevant transforms, the image size and bits per pixel are also explored. Using the (n, n) block of transform, adaptive scanning is used to acquire the best feasible compression ratio. Because of these characteristics, multimedia systems and services have a wide range of possible applications.

Modulation identification and the recovery of the carrier lead to many applications in the digital communication system. Techniques developed so far for carrier recovery and modulation identification are all statistical. In this paper efforts are made to use a deterministic method rather than statistical. Zero crossing detection method is recommended for use to recover carrier from frequency and amplitude modulated systems. In this paper some modifications have been suggested for the application of the same system to make it useful for QAM and PSK as well. Based on the method presented by R. W. Wall, a modified Zero Crossing Detection algorithm has been used for carrier and phase extraction from an M-ary PSK (differential and non-differential) system. In addition, method has been developed for M-ary PSK system identification.

This paper investigates the error probability of our proposed modulation technique which is produced by combining the existing pulse position modulation (PPM) technique with Hadamard code (HC). Moreover, the HC is also combined with differential PPM (DPPM). The error performances of existing technique and the proposed technique are compared at high-order modulation (HOM) and low-order modulation. The results show that the error performance of the proposed technique outperforms at HOM at the cost of higher bandwidth. This error performance is improved due to the bipolar nature of HC and it reduces the power requirements of the system.