International Conference on Recent Trends in Information Technology and Computer Science (IRCTITCS) 2011 Proceedings published in International Journal of Computer Applications® (IJCA) 23 Advanced Video Compression Technique of H.264 Codec Using SPIHT Algorithm S.K Singh Research Scholar-Mukesh Patel School of Technology Management and Engineering NMIMS University, Santacruz Mumbai-400056 Mahendra Sharma Thakur College of Science and Commerce Thakur Village Kandivali (East) Mumbai -400101 Priti Singh and Greta Dabre PG Student -Thakur College of Science and Commerce, Thakur Village, Kandivali (East) Mumbai -400101 ABSTRACT The ever increasing bandwidth requirements for transmission of video signals in mobile and internet environment has necessitated video compression and attempts to compare the low bit rate characteristics of the major video compression methods. This paper makes use of H.264 using DCT and wavelet based video compression. Also attempts are made to compare the results of these methods. Initially the compressed signal / data is transmitted and the receiving end the video signals are reconstructed. This paper implements SPIHT(Set Partitioning in Hierarchical Trees) algorithm for video compression. The algorithm codes the most important wavelet transform coefficients first, and transmits the bits so that an increasingly refined copy of the original video can be obtained progressively. Keywords H.264, DCT, SPIHT, Video Compression, Wavelet, Low Bit Rate, Matlab, etc. 1. INTRODUCTION Despite rapid progress in mass-storage density, processor speeds, and digital communication system performance, demand for data storage capacity and data-transmission bandwidth continues to outstrip the capabilities of available technologies. The recent growth of data intensive multimedia- based web applications have not only sustained the need for more efficient ways to encode signals and images but have made compression of such signals central to storage and communication technology. Compression is useful because it helps reduce the consumption of expensive resources, such as hard disk space or transmission bandwidth. On the downside, compressed data must be decompressed to be used, and this extra processing may be detrimental to some applications. Compressed video can effectively reduce the bandwidth required to transmit video via terrestrial broadcast, via cable TV, or via satellite TVservices. Most video compression is lossy it operates on the premise that much of the data present before compression is not necessary for achieving good perceptual quality. Video is basically a three-dimensional array of color pixels. Two dimensions serve as spatial (horizontal and vertical) directions of the moving pictures, and one dimension represents the time domain. A data frame is a set of all pixels that correspond to a single time moment. Basically, a frame is the same as a still picture. The performance of H.264 generally degrades at low bit-rates mainly because of the underlying block-based Discrete Cosine Transform (DCT) scheme. More recently, the wavelet transform has emerged as a cutting edge technology, within the field of image & video compression. Many efforts have been taken in past to discuss image compression techniques [3] [6]. 2. PROBLEM STATEMENT Discrete Cosine Transformation is mainly used for image, video compression but it has several disadvantages such as  Only spatial correlation of the pixels inside the single 2-D block is considered and the correlation from the pixels of the neighboring blocks is neglected.  Undesirable blocking artifacts affect the reconstructed images or video frames. (High compression ratios or very low bit rates).  DCT function is fixed. i.e. it cannot be adapted to source data  Does not perform efficiently for binary images (fax or pictures of fingerprints) characterized by large periods of constant amplitude, followed by brief periods of sharp transitions. So, because of all this reasons DCT does not provide efficient image/video compression and we may get noisy image while decompressing data. 3. WAVELET Wavelet can often compress or de-noise a signal without appreciable degradation. Wavelet transforms are broadly divided into three classes: the continuous wavelet transform, the discredited wavelet transform and multi-resolution based wavelet transform. DWT is good for signal having high frequency components for short durations and low frequency components for long duration e.g. images. When a wavelet transform of the image is performed, a coefficient in a low sub- band can be thought of having four descendants in the next higher sub-band. The four descendants each have four descendants in the next higher sub-band. Discrete wavelet transform (DWT), transforms a discrete time signal to a discrete wavelet representation [1] [4]. A 2D wavelet transforms works as follows [5]: Figure 1: Wavelet Decomposition