This paper introduces a novel deep learned quantization-based coding for 3D Airborne LiDAR (Light detection and ranging) point cloud (pcd) image (DLQCPCD). The raw pcd signals are sampled and transformed by applying the Nyquist signal... more
This paper introduces a novel deep learned quantization-based coding for 3D Airborne LiDAR (Light detection and ranging) point cloud (pcd) image (DLQCPCD). The raw pcd signals are sampled and transformed by applying the Nyquist signal sampling and Min-max signal transformation techniques, respectively for improving the efficiency of the training process. Then, the transformed signals are feed into the deep learned quantization module for compressing the data. To the best of our knowledge, this proposed DLQCPCD is the first deep learning-based model for 3D airborne LiDAR pcd compression. The functions of Mean Squared Error and Stochastic Gradient Descent optimization function enhance the quality of the decompressed image by 67.01 percent on average, compared to other functions. The model’s efficiency has been validated with established well-known compression techniques such as the 7-Zip, WinRAR, and tensor tucker decomposition algorithm on the three inconsistent airborne datasets. Th...
This paper presents the effective lossy and lossless color image compression algorithm with Multilayer perceptron. The parallel structure of neural network and the concept of image compression combined to yield a better reconstructed... more
This paper presents the effective lossy and lossless color image compression algorithm with Multilayer perceptron. The parallel structure of neural network and the concept of image compression combined to yield a better reconstructed image with constant bit rate and less computation complexity. Original color image component has been divided into 8x8 blocks. The discrete cosine transform (DCT) applied on each block for lossy compression or discrete wavelet transform (DWT) applied for lossless image compression. The output coefficient values have been normalized by using mod function. These normalized vectors have been passed to Multilayer Perceptron (MLP). This proposed method implements the Back propagation neural network (BPNN) which is suitable for compression process with less convergence time. Performance of the proposed compression work is evaluated based on three ways. First one compared the performance of lossy and lossless compression with BPNN. Second one, evaluated based ...
Image carries more information about the ideas than text. Growth of social media, images has become the universal language because it is more interactive. Images are used in different fields like medical, multimedia, industries etc. When... more
Image carries more information about the ideas than text. Growth of social media, images has become the universal language because it is more interactive. Images are used in different fields like medical, multimedia, industries etc. When using the images, we need to find effective storage and transmission methods to reduce storage size and transmission time. Lossless and lossy are two ways to compress the image to reduce the storage and transmission time. The proposed method implements the concept of lossless image compression using the method of Kronecker delta notation, wavelet based on Birge-Massart strategy and parity strategy. This paper presents that enhancing the image by applying the Kronecker delta notation as the mask and applying the wavelet based on Birge-Massart strategy, finally applying the parity threshold to compress the image. The proposed method is compared the compression ratio (CR) with the existing lossless compression methods such as Birge – Massart without th...
