Insect navigation arises from the coordinated action of concurrent guidance systems but the neural mechanisms through which each functions, and are then coordinated, remains unknown We propose that insects require distinct strategies to retrace familiar routes (route-following) and directly return from novel to familiar terrain (homing) using different aspects of frequency encoded views that are processed in different neural pathways We also demonstrate how the Central Complex and Mushroom Bodies regions of the insect brain may work in tandem to coordinate the directional output of different guidance cues through a contextually switched ring-attractor inspired by neural recordings The resultant unified model of insect navigation reproduces behavioural data from a series of cue conflict experiments in realistic animal environments and offers testable hypotheses of where and how insects process visual cues, utilise the different information that they provide and coordinate their outputs to achieve the adaptive behaviours observed in the wild

A decentralised neural model explaining optimal integration of navigational strategies in insects.

Neural compression is the application of neural networks and other machine learning methods to data compression. Recent advances in statistical machine learning have opened up new possibilities for data compression, allowing compression algorithms to be learned end-to-end from data using powerful generative models such as normalizing flows, variational autoencoders, diffusion probabilistic models, and generative adversarial networks. The present article aims to introduce this field of research to a broader machine learning audience by reviewing the necessary background in information theory (e.g., entropy coding, rate-distortion theory) and computer vision (e.g., image quality assessment, perceptual metrics), and providing a curated guide through the essential ideas and methods in the literature thus far.

An Introduction to Neural Data Compression

Dynamic point cloud (DPC) captures 3D object and scene with realistic appearance to mimic the natural reality. It inherently offers the six-degree-of-freedom (6DoF) for content consumption, which motivates us to facilitate the network-friendly view-dependent DPC streaming by leveraging the limited field of view of the human visual system (HVS) at specific moments, for significant network bandwidth reduction without the quality of experience (QoE) loss. Therefore, in this work, we propose the view-dependent DPC compression, noted as View-PCC, by which we can enable instantaneous and complete view reconstruction from partial streams, and 6DoF navigation by stream adaptation. These functionalities are offered by the hybrid global and local projection to effectively map 3D points to five perpendicular 2D image planes of a cube. Multi-view and multi-layer based global projection is first applied progressively and followed by the patch-based local projection with both intra-frame displaced arrangement and inter-frame temporal alignment, so as to maximize the number of effectively projected points and preserve the spatio-temporal coherency for better compression. Boundary padding is then augmented for each image plane that will be encoded using the successful 2D video coding standard - High-Efficiency Video Coding (HEVC). Our extensive simulations have shown the noticeable objective compression gains of View-PCC when compared with the typical octree-based approach and global-projection-based method. The subjective quality assessment suggests the better reconstruction quality of View-PCC, in comparison to the MPEG video-based point cloud compression (V-PCC).

View-Dependent Dynamic Point Cloud Compression

Robust insect navigation arises from the coordinated action of concurrent guidance systems but the neural mechanisms through which they function, and are coordinated, remain unknown. Our analysis suggests that insects require distinct visual homing and route following strategies which we propose use different aspects of frequency encoded views and different neural pathways: Mushroom Bodies for visual homing and Anterior Optic Tubercles for route following. We then demonstrate how the Central Complex and Mushroom Bodies work in tandem to coordinate the directional output of different guidance cues through contextually switched ring-attractor circuits inspired by neural recordings. The resultant unified model of insect navigation reproduces behavioral data from a series of cue conflict experiments in realistic animal environments and offers testable hypotheses of where and how insects process visual cues, utilise the different information provided and coordinate their outputs to achieve the adaptive behaviours observed in the wild.

https://www.biorxiv.org/content/biorxiv/early/2020/03/05/856153.full.pdf

A Decentralised Neural Model Explaining Optimal Integration Of Navigational Strategies in Insects

A new hybrid transform for lossless image compression exploiting a discrete wavelet transform (DWT) and prediction is the main new contribution of this paper. Simple prediction is generally considered ineffective in conjunction with DWT but we applied it to subbands of DWT modified using reversible denoising and lifting steps (RDLSs) with step skipping. The new transform was constructed in an image-adaptive way using heuristics and entropy estimation. For a large and diverse test set consisting of 499 photographic and 247 non-photographic (screen content) images, we found that RDLS with step skipping allowed effectively combining DWT with prediction. Using prediction, we nearly doubled the JPEG 2000 compression ratio improvements that could be obtained using RDLS with step skipping. Because for some images it might be better to apply prediction instead of DWT, we proposed compression schemes with various tradeoffs, which are practical contributions of this study. Compared with unmodified JPEG 2000, one scheme improved the compression ratios of photographic and non-photographic images, on average, by 1.2% and 30.9%, respectively, at the cost of increasing the compression time by 2% and introducing only minimal modifications to JPEG 2000. Greater ratio improvements, exceeding 2% and 32%, respectively, are attainable at a greater cost.

https://www.mdpi.com/1099-4300/22/7/751/pdf

Hybrid Adaptive Lossless Image Compression Based on Discrete Wavelet Transform

Digital image capture, processing, storage, transmission, and display are now taken for granted as part of the technology of modern everyday life. Digital image compression is one of the enabling technologies of the present multimedia world. The image compression technique used for application as diverse as photography, web pages, medical imaging, and public records is JPEG, named after the ISO/CCITT “joint photographic experts group,” established in 1986, which developed the technique in the late 1980s and produced the international standard in the early ’90s. ITU-T T.81¦ISO/IEC 10918-1, also called “JPEG-1” has become one of the most successful standards in information and communication technologies (ICT) history. The authors of this paper—all members of the original JPEG development team—were all intimately involved in image-coding research and JPEG in particular. The paper goes behind the scenes explaining why and how JPEG came about and looks under the bonnet of the technique explaining the different components that give the standard of its efficiency, versatility, and robustness that have made a technique that has stood the test of time and evolved to cover applications beyond its original scope. In addition, the authors give a short outlook of the main milestones in coding schemes of still images since “JPEG-1.”

https://www.spiedigitallibrary.org/journals/journal-of-electronic-imaging/volume-27/issue-4/040901/JPEG-1-standard-25-years--past-present-and-future/10.1117/1.JEI.27.4.040901.pdf

JPEG-1 standard 25 years: past, present, and future reasons for a success

JPEG is celebrating the 25th anniversary of its approval as a standard this year. Where did JPEG come from, and what are the fundamental components that have given it longevity?

Alain Leger

Papers

JPEG-1 standard 25 years: past, present, and future reasons for a success

JPEG at 25: Still Going Strong