Linear and nonlinear temporal prediction employing lifting structures for scalable video coding

Scalable 3D video codecs based on wavelet lifting structures have attracted recently a lot of attention, due to their compression performance comparable with that of state-of-art hybrid codecs. In this work, we propose a set of linear and nonlinear predictors for the temporal prediction step in lifting implementation. The predictor uses pixels on the motion trajectories of the frames in a window around the pixel to be predicted to improve the quality of prediction. Experimental results show that the video quality as well as PSNR values are improved with the proposed prediction method

Hybrid algorithms for spectral noise removal in hyper spectral images

The image acquired from a sensor is always degraded by some form of noise. The noise can be estimated and removed by the process of denoising. Recently, the use of Hybrid Algorithms for denoising has gained popularity. The most commonly used transformation are Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT). DCT has the property of more energy compaction and requires less resources for computational whereas DWT is a multi-resolution transformation. The proposed Hybrid Algorithms take advantage of both of the algorithms and this reduces the false contouring and blocking articrafts effectively. In this paper, the Hybrid Algorithms are evaluated for various images by differentiating with respect to Mean Square Error, Peak Signal to Noise Ratio, Coefficient of Variance, Structural Similarity Index and Mean Structural Similarity Index

1d & 2d Signal Compression Using Discrete Wavelet Transform : A Survey

Today’s smart world with high-speed communication devices demands elegant computing systems with lightening speed. Compression technology takes a major part in developing new generation computing systems. Popular applications like multimedia and medical data processing technology desires high data transmission rate, good perceptual signal quality and high compression rates. Wavelet based data compression techniques have advantages in lossless signal reconstructions and fit in dedicated data processing field. This paper highlights some wavelet transform based compression algorithms implementation and measuring performance towards quality of reconstruction and compression rate of one and two dimensional signal

OPTIMISED COMPRESSION STRATEGY IN WAVELET-BASED VIDEO CODING USING IMPROVED CONTEXT MODELS

ABSTRACT Accurate probability estimation is a key to efficient compression in entropy coding phase of state-of-the-art video coding systems. Probability estimation can be enhanced if contexts in which symbols occur are used during the probability estimation phase. However, these contexts have to be carefully designed in order to avoid negative effects. Methods that use tree structures to model contexts of various syntax elements have been proven efficient in image and video coding. In this paper we use such structure to build optimised contexts for application in scalable wavelet-based video coding. With the proposed approach context are designed separately for intra-coded frames and motion-compensated frames considering varying statistics across different spatio-temporal subbands. Moreover, contexts are separately designed for different bit-planes. Comparison with compression using fixed contexts from Embedded ZeroBlock Coding (EZBC) has been performed showing improvements when context modelling on tree structures is applied

Hierarchical quantization indexing for wavelet and wavelet packet image coding

In this paper, we introduce the quantization index hierarchy, which is used for efficient coding of quantized wavelet and wavelet packet coefficients. A hierarchical classification map is defined in each wavelet subband, which describes the quantized data through a series of index classes. Going from bottom to the top of the tree, neighboring coefficients are combined to form classes that represent some statistics of the quantization indices of these coefficients. Higher levels of the tree are constructed iteratively by repeating this class assignment to partition the coefficients into larger Subsets. The class assignments are optimized using a rate-distortion cost analysis. The optimized tree is coded hierarchically from top to bottom by coding the class membership information at each level of the tree. Context-adaptive arithmetic coding is used to improve coding efficiency. The developed algorithm produces PSNR results that are better than the state-of-art wavelet-based and wavelet packet-based coders in literature.This research was supported by Isik University BAP-05B302 GrantPublisher's Versio

Hybrid DWT-DCT algorithm for image and video compression applications

Digital image and video in their raw form require an enormous amount of storage capacity. Considering the important role played by digital imaging and video, it is necessary to develop a system that produces high degree of compression while preserving critical image/video information. There are various transformation techniques used for data compression. Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) are the most commonly used transformation. DCT has high energy compaction property and requires less computational resources. On the other hand, DWT is multiresolution transformation. In this work, we propose a hybrid DWT-DCT algorithm for image compression and reconstruction taking benefit from the advantages of both algorithms. The algorithm performs the Discrete Cosine Transform (DCT) on the Discrete Wavelet Transform (DWT) coefficients. Simulations have been conducted on several natural, benchmark, medical and endoscopic images. Several QCIF, high definition, and endoscopic videos have also been used to demonstrate the advantage of the proposed scheme. The simulation results show that the proposed hybrid DWT-DCT algorithm performs much better than the standalone JPEG-based DCT, DWT, and WHT algorithms in terms of peak signal to noise ratio (PSNR), as well as visual perception at higher compression ratio. The new scheme reduces “false contouring” and “blocking artifacts” significantly. The rate distortion analysis shows that for a fixed level of distortion, the number of bits required to transmit the hybrid coefficients would be less than those required for other schemes Furthermore, the proposed algorithm is also compared with the some existing hybrid algorithms. The comparison results show that, the proposed hybrid algorithm has better performance and reconstruction quality. The proposed scheme is intended to be used as the image/video compressor engine in imaging and video applications