Abstract-As a rule, a video signal has high temporal redundancies due to the high correlation between successive frames. This redundancy has not been deflated enough by current video compression techniques. In this paper, we present a new video compression technique which tends to hard exploit the relevant temporal redundancy in the video to improve solidity efficiency with minimum processing complexity. It includes 3D (Three Dimension) to 2D (Three Dimension) transformation of the video that allows exploring the temporal redundancy of the video using 2D transforms and avoiding the computationally demanding motion recompense step. This transformation converts the spatial and temporal correlation of the video signal into a high spatial correlation. Indeed, this technique transforms each group of pictures into one picture eventually with high spatial correlation. SPIHT (Set Partitioning in Hierarchical Trees) exploits the properties of the wavelet-transformed images to increase its efficiency. Thus, the De-correlation of the resulting pictures by the DWT (Discrete Wavelet Transform) makes efficient energy compaction, and therefore produces a high video compression ratio. Many experimental tests had been conducted to prove the technique efficiency especially in high bit rate and with slow motion video.
Intensity non-uniformity or intensity inhomogeneity usually occurs in Real world Images, those images cannot be segmented by using image segmentation. The most commonly used algorithms in image segmentation are region based and depends on the homogeneity of the image intensities which usually fails to produce accurate segmentation results due to the intensity non-uniformity. In this paper we proposed a novel region based method for image segmentation which can be able to discuss with intensity non-uniformities in image segmentation. First according to the image models with intensity non-uniformities we define a local clustering criterion function for the intensities in the image neighbourhood of each part. The local clustering criterion function is then integrated with respect to the neighbourhood center to give a global criterion of image segmentation. In a level set formulation this criterion defines an energy in terms of level set functions that represents the partition of image domain and a bias field that corresponds to the intensity non-uniformity of the image. Therefore, by minimizing the energy we can able to segment the image simultaneously and estimate the bias field can be used for the intensity non-uniformity correction. This method is applied on MRI images and real world images of various modalities with desirable performance in the presence of intensity nonuniformities. The experiment results show that the method is stronger, faster and more accurate than the wellknown piecewise smooth model and gives promising results. As an application this method is used for segmentation and bias correction of real world images and MRI images with better results.
Wavelets are being suggested as a platform for various tasks in image processing. The advantage of wavelets lie in its time frequency resolution. The use of different basis functions in the form of different wavelets made the wavelet analysis a destination for many applications. The performance of a particular technique depends on the wavelet coefficients arrived after applying the wavelet transform. The coefficients for a specific input signal depends on the basis functions used in the wavelet transform. Hence, in this paper toward this end, different basis functions and their features are presented. As many image comprssion algorithms base on wavelet transform, selection of basis function for image compression should be taken with care. In this paper, the factors influencing the performance of image compression are presented. In addition to this, a broad review of wavelets in image processing applications and selection of basis function for different image processing tasks are presented.
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