Image compression is playing a vital role in the development of various multimedia applications. Image Compression solves the problem of reducing the amount of data required to represent the digital image. In image compression methods there are several techniques evolved. All techniques of image compression basically divided into two parts, spatial domain compression technique and frequency domain compression technique. In frequency domain techniques there are numerous techniques like Fourier Transform, Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) etc. after converting the image into frequency domain transformation, it uses several encoding technique like Embedded Zero Tree (EZW) coding, SPIHT (Set Partitioning in Hierarchical Tree), ASWDR (Adaptively Scanned Wavelet Difference Reduction) WDR (Wavelet Difference Reduction) and STW (Spatial orientation Tree Wavelet) etc. These encoding schemes are also known as Progressive Coefficients Significance Methods (PCSM). In this paper the wavelet filters combine with improved PCSM codes and proposed a new filter for further improvement. In new wavelet proposed filter has slightly change in the scaling and wavelet function of existing filter. It gives the wide range of selectivity of frequencies in higher and lower side of it. Hence it provides better lower bandwidth range with greater high preservation of frequencies. Scaling and wavelet impulse response of proposed filter then a comparison is made on the proposed work with all the filters. Filters are demonstrated to show the performance of compression using wavelet functions. The filters are used in the work like bi-orthogonal (BIO), Reverse bi-orthogonal (RBIO), Coiflets (COIF), Daubechies (DB), Symlet (SYM) and Improved Progressive Coefficients Significance Method (IPCSM) encoding scheme will be compare and analyze with all compression parameters like mean square error (MSE), peak to signal noise ratio (PSNR), compression ratio (CR), execution time (ET), bits per pixel (BPP), root mean square error (MSE). 

Visual surveillance System is used for analysis and interpretation of object behaviors. It involves object classification to understand the visual events in videos. In this review paper various object classification methods are used. Classification technique plays an important role in surveillance system that is used for the classification of both objects like static and moving objects in a better way. The methods in object classification are used to extract meaningful information and various features that are needed for representation of data. In this survey, we described various approaches for moving objects that are used in classification for video surveillance system based on shape and motion.