TEMPORAL REDUNDANCY REDUCTION IN WAVELET BASED VIDEO COMPRESSION FOR HIGH DEFINITION VIDEOS

Data Storage and Communication plays a significant role in every human. Digital images and videos are stored in mobile and other storage devices. More specifically, video data requires huge amount of storage space for which the storage devices are more expensive. Hence there is a necessity of reducing the storage space of the data. Video compression is more common in all researches. In this work, the role of wavelets in video compression is studied. The temporal redundant data are converted to spatial data which are then transformed to wavelet coefficients. The low frequency components are removed from these wavelet coefficients. The proposed method is tested with some video sequences. The performance of the proposed method is analyzed by comparing it with the existing recent methods and with the state-of-art H.265 video coding standard. The experimental results substantially proved that the proposed method achieves 3.8dB higher PSNR than H.265 and 1.6dB higher PSNR than recent wavelet based video codecs

IMPROVING EFFICIENCY IN IMAGE ENCRYPTION AND COMPRESSION USING PERMUTATIONS & PREDICTIONS

Due to rapid growth in image sizes, an alternate of numerically lossless coding named visually lossless coding is considered to reduce storage size and lower data transmission. In this paper, a lossy compression method on encrypted color image is introduced with undetectable quality loss and high compression ratio. The proposed method includes the Zhang lossy compression [1], Hierarchical Oriented Prediction (HOP) [2], uniform quantization, negative sign removal, concatenation of 7-bit data and Huffman compression. The encrypted image is divided into rigid and elastic parts. The Zhang elastic compression is applied on elastic part and HOP is applied on rigid part. This method is applied on different test cases and the results were evaluated. The experimental evidences suggest that, the proposed method has better coding performance than the existing encrypted image compressions, with 9.645 % reductions in bit rate and the eye perception is visually lossless

Shot Boundary Detection Using Octagon Square Search Pattern

In this paper, a shot boundary detection method is presented using octagon square search pattern. The color, edge, motion and texture features of each frame are extracted and used in shot boundary detection. The motion feature is extracted using octagon square search pattern. Then, the transition detection method is capable of detecting the shot or non-shot boundaries in the video using the feature weight values. Experimental results are evaluated in TRECVID video test set containing various types of shot transition with lighting effects, object and camera movement within the shots. Further, this paper compares the experimental results of the proposed method with existing methods. It shows that the proposed method outperforms the state-of-art methods for shot boundary detection

Local Pattern Transformation Based Feature Extraction for Recognition of Parkinson’s Disease Based on Gait Signals

Parkinson’s disease (PD) is a neuro-degenerative disorder primarily triggered due to the deterioration of dopamine-producing neurons in the substantia nigra of the human brain. The early detection of Parkinson’s disease can assist in preventing deteriorating health. This paper analyzes human gait signals using Local Binary Pattern (LBP) techniques during feature extraction before classification. Supplementary to the LBP techniques, Local Gradient Pattern (LGP), Local Neighbour Descriptive Pattern (LNDP), and Local Neighbour Gradient Pattern (LNGP) were utilized to extract features from gait signals. The statistical features were derived and analyzed, and the statistical Kruskal–Wallis test was carried out for the selection of an optimal feature set. The classification was then carried out by an Artificial Neural Network (ANN) for the identified feature set. The proposed Symmetrically Weighted Local Neighbour Gradient Pattern (SWLNGP) method achieves a better performance, with 96.28% accuracy, 96.57% sensitivity, and 95.94% specificity. This study suggests that SWLNGP could be an effective feature extraction technique for the recognition of Parkinsonian gait