Discovering cause and effect variables from observational data is an important but challenging problem in science and engineering. In this work, a recently proposed brain inspired learning algorithm namely-\emph{Neurochaos Learning} (NL) is used for the classification of cause and effect time series generated using coupled autoregressive processes, coupled 1D chaotic skew tent maps, coupled 1D chaotic logistic maps and a real-world prey-predator system. In the case of coupled skew tent maps, the proposed method consistently outperforms a five layer Deep Neural Network (DNN) and Long Short Term Memory (LSTM) architecture for unidirectional coupling coefficient values ranging from $0.1$ to $0.7$. Further, we investigate the preservation of causality in the feature extracted space of NL using Granger Causality for coupled autoregressive processes and Compression-Complexity Causality for coupled chaotic systems and real-world prey-predator dataset. Unlike DNN, LSTM and 1D Convolutional Neural Network, it is found that NL preserves the inherent causal structures present in the input timeseries data. These findings are promising for the theory and applications of causal machine learning and open up the possibility to explore the potential of NL for more sophisticated causal learning tasks.

Set Partitioned Embedded bloCK coder (SPECK) has been found to perform comparable to the best-known still grayscale image coders like EZW, SPIHT, JPEG2000 etc. In this paper, we first propose Color-SPECK (CSPECK), a natural extension of SPECK to handle color still images in the YUV 4:2:0 format. Extensions to other YUV formats are also possible. PSNR results indicate that CSPECK is among the best known color coders while the perceptual quality of reconstruction is superior than SPIHT and JPEG2000. We then propose a moving picture based coding system called Motion-SPECK with CSPECK as the core algorithm in an intra-based setting. Specifically, we demonstrate two modes of operation of Motion-SPECK, namely the constant-rate mode where every frame is coded at the same bit-rate and the constant-distortion mode, where we ensure the same quality for each frame. Results on well-known CIF sequences indicate that Motion-SPECK performs comparable to Motion-JPEG2000 while the visual quality of the sequence is in general superior. Both CSPECK and Motion-SPECK automatically inherit all the desirable features of SPECK such as embeddedness, low computational complexity, highly efficient performance, fast decoding and low dynamic memory requirements. The intended applications of Motion-SPECK would be high-end and emerging video applications such as High Quality Digital Video Recording System, Internet Video, Medical Imaging etc.

We propose an embedded, block-based, image wavelet transform coding algorithm of low complexity. It uses a recursive set-partitioning procedure to sort subsets of wavelet coefficients by maximum magnitude with respect to thresholds that are integer powers of two. It exploits two fundamental characteristics of an image transform-the well-defined hierarchical structure, and energy clustering in frequency and in space. The two partition strategies allow for versatile and efficient coding of several image transform structures, including dyadic, blocks inside subbands, wavelet packets, and discrete cosine transform (DCT). We describe the use of this coding algorithm in several implementations, including reversible (lossless) coding and its adaptation for color images, and show extensive comparisons with other state-of-the-art coders, such as set partitioning in hierarchical trees (SPIHT) and JPEG2000. We conclude that this algorithm, in addition to being very flexible, retains all the desirable features of these algorithms and is highly competitive to them in compression efficiency.