FID-calibrated simultaneous multi-slice fast spin echo with long trains of hard pulses

Objective: To develop a novel, free-induction-decay (FID)-calibrated single-shot simultaneous multi-slice fast spin echo (SMS-FSE) with very long hard pulse trains for high encoding efficiency and low energy deposition. Approach: The proposed single-shot SMS-FSE employs a mixed pulse configuration in which a long excitation pulse that is spatially multi-band (MB) selective is used in conjunction with short spatially nonselective refocusing pulses. To alleviate energy deposition to tissues while reducing signal modulation along the echo train, variable low flip angles with signal prescription are utilized in the refocusing pulse train. A time-efficient FID-calibration and correction method is introduced before aliased voxels in the slice direction are resolved. Simulations and experiments are performed to demonstrate the feasibility of the proposed method as an alternative to conventional HASTE for generating T2-weighted images. Main results: Compared with conventional HASTE, the proposed method enhances imaging speed effectively by an MB factor up to 5 without apparent loss of image contrast while successfully eliminating FID artifacts. Significance: We successfully demonstrated the feasibility of the proposed method as an encoding- and energy-efficient alternative to conventional HASTE for generation of T2-weighted contrast.

Motion Estimation in HEVC/H.265: Metaheuristic Approach to Improve the Efficiency

An ever increasing use of digital video applications such as video telephony, broadcast and the storage of high and ultra-high definition videos has steered the development of video coding standards. The state of the art video coding standard is High Efficiency Video Coding (HEVC) or otherwise known as H.265. It promises to be 50 percent more efficient than the previous video coding standard H.264. Ultimately, H.265 provides significant improvement in compression at the expense of computational complexity. HEVC encoder is very complex and 50 percent of the encoding consists of Motion Estimation (ME). It uses a Test Zone (TZ) fast search algorithm for its motion estimation, which compares a block of pixels with a few selected blocks in the search region of a referenced frame. However, the encoding time is not suitable to meet the needs of real time video applications. So, there is a requirement to improve the search algorithm and to provide comparable results to TZ search to save a substantial amount of time. In our paper, we aim to study the effects of a meta-heuristic algorithm on motion estimation. One such suitable algorithm for this task is the Firefly Algorithm (FA). FA is inspired by the social behavior of fireflies and is generally used to solve optimization problems. Our results show that implementing FA for ME saves a considerable amount of time with a comparable encoding efficiency.

Audiovisual task switching rapidly modulates sound encoding in mouse auditory cortex

In everyday behavior, sensory systems are in constant competition for attentional resources, but the cellular and circuit-level mechanisms of modality-selective attention remain largely uninvestigated. We conducted translaminar recordings in mouse auditory cortex (AC) during an audiovisual (AV) attention shifting task. Attending to sound elements in an AV stream reduced both pre-stimulus and stimulus-evoked spiking activity, primarily in deep layer neurons. Despite reduced spiking, stimulus decoder accuracy was preserved, suggesting improved sound encoding efficiency. Similarly, task-irrelevant probe stimuli during intertrial intervals evoked fewer spikes without impairing stimulus encoding, indicating that these attention influences generalized beyond training stimuli. Importantly, these spiking reductions predicted trial-to-trial behavioral accuracy during auditory attention, but not visual attention. Together, these findings suggest auditory attention facilitates sound discrimination by filtering sound-irrelevant spiking in AC, and that the deepest cortical layers may serve as a hub for integrating extramodal contextual information.

Flow-based Intrinsic Curiosity Module

In this paper, we focus on a prediction-based novelty estimation strategy upon the deep reinforcement learning (DRL) framework, and present a flow-based intrinsic curiosity module (FICM) to exploit the prediction errors from optical flow estimation as exploration bonuses. We propose the concept of leveraging motion features captured between consecutive observations to evaluate the novelty of observations in an environment. FICM encourages a DRL agent to explore observations with unfamiliar motion features, and requires only two consecutive frames to obtain sufficient information when estimating the novelty. We evaluate our method and compare it with a number of existing methods on multiple benchmark environments, including Atari games, Super Mario Bros., and ViZDoom. We demonstrate that FICM is favorable to tasks or environments featuring moving objects, which allow FICM to utilize the motion features between consecutive observations. We further ablatively analyze the encoding efficiency of FICM, and discuss its applicable domains comprehensively. See here for our codes and demo videos.

Рекуррентные соотношения для оценки параметров транспортных подсистем кодирования и передачи изображений

Кодирование видеоинформации обычно относится к вычислительной технологии, которая сжимает изображение и преобразует его в двоичный код (т.е. биты), чтобы уменьшить объём информации с целью хранения и передачи. Часто на практике кодирование приводит к потере качества, поэтому оценка эффективности кодирования осуществляется в двух аспектах: во-первых, это скорость передачи, измеряемая в битах, чем меньше, тем лучше во-вторых, понесенные потери, измеряемые субъективным качеством восстановленной видеоинформации, чем выше качество, тем лучше. Современные решения в сфере многоуровневого кодирования видеоинформации представляют стандарты MPEGx, использующие предсказание движения, и относятся к классу, так называемых, трудно формализуемых задач. Эти стандарты исчерпали себя и необходимы новые подходы, позволяющие увеличить эффективность кодирования путем не только максимального приближения к эпсилон-энтропии источника, но и какими информационными средствами достигнуто данное приближение и, в первую очередь, с какими точностью, скоростью и сложностью видеосистем. В работе рассматриваются вопросы, связанные с оценкой количества информации, необходимой для передачи характерных точек объектов на изображениях путём рекурсивного разбиения на полигоны различной формы и размера. Разработаны рекуррентные соотношения для оценки зависимости количества информации для кодирования характерных точек изображений от таких параметров, как разрядности данных, числа полигонов после разбиения, размера и формата изображений.Video encoding typically refers to computing technology that compresses an image and converts it into binary code (i.e. bits) to reduce the amount of information for the purpose of storage and transmission. Often in practice, encoding leads to loss of quality, therefore, the evaluation of encoding efficiency is carried out in two aspects: first, it is the transmission rate measured in bits, the less the better second, the losses incurred, measured by the subjective quality of the restored video information, the higher the quality, the better. Modern solutions in the field of multilevel encoding of video information are MPEGx standards that use motion prediction belong to the class of so-called, difficult to formalize problems. These standards are exhausted and need new approaches to increase the coding efficiency by not only the closest approach to Epsilon-entropy source, but what the information means to achieve this approach and, primarily, the precision, speed and complexity of video systems. The paper deals with the issues related to the assessment of the amount of information required to transfer the characteristic points of objects in the images by recursive partitioning into polygons of different shapes and sizes. Recurrence relations are developed to estimate the dependence of the amount of information to encode the characteristic points of images on such parameters as the bit depth of data, the number of polygons after splitting, the size and format of images.

Optical Flow-Based Fast Motion Parameters Estimation for Affine Motion Compensation

This study proposes a lightweight solution to estimate affine parameters in affine motion compensation. Most of the current approaches start with an initial approximation based on the standard motion estimation, which only estimates the translation parameters. From there, iterative methods are used to find the best parameters, but they require a significant amount of time. The proposed method aims to speed up the process in two ways, first, skip evaluating affine prediction when it is likely to bring no encoding efficiency benefit, and second, by estimating better initial values for the iteration process. We use the optical flow between the reference picture and the current picture to estimate quickly the best encoding mode and get a better initial estimation. We achieve a reduction in encoding time over the reference of half when compared to the state of the art, with a loss in efficiency below 1%.

Context-Aware Superpixel and Bilateral Entropy—Image Coherence Induces Less Entropy

Superpixel clustering is one of the most popular computer vision techniques that aggregates coherent pixels into perceptually meaningful groups, taking inspiration from Gestalt grouping rules. However, due to brain complexity, the underlying mechanisms of such perceptual rules are unclear. Thus, conventional superpixel methods do not completely follow them and merely generate a flat image partition rather than hierarchical ones like a human does. In addition, those methods need to initialize the total number of superpixels, which may not suit diverse images. In this paper, we first propose context-aware superpixel (CASP) that follows both Gestalt grouping rules and the top-down hierarchical principle. Thus, CASP enables to adapt the total number of superpixels to specific images automatically. Next, we propose bilateral entropy, with two aspects conditional intensity entropy and spatial occupation entropy, to evaluate the encoding efficiency of image coherence. Extensive experiments demonstrate CASP achieves better superpixel segmentation performance and less entropy than baseline methods. More than that, using Pearson’s correlation coefficient, a collection of data with a total of 120 samples demonstrates a strong correlation between local image coherence and superpixel segmentation performance. Our results inversely support the reliability of above-mentioned perceptual rules, and eventually, we suggest designing novel entropy criteria to test the encoding efficiency of more complex patterns.

Different languages, similar encoding efficiency: Comparable information rates across the human communicative niche

Language is universal, but it has few indisputably universal characteristics, with cross-linguistic variation being the norm. For example, languages differ greatly in the number of syllables they allow, resulting in large variation in the Shannon information per syllable. Nevertheless, all natural languages allow their speakers to efficiently encode and transmit information. We show here, using quantitative methods on a large cross-linguistic corpus of 17 languages, that the coupling between language-level (information per syllable) and speaker-level (speech rate) properties results in languages encoding similar information rates (~39 bits/s) despite wide differences in each property individually: Languages are more similar in information rates than in Shannon information or speech rate. These findings highlight the intimate feedback loops between languages’ structural properties and their speakers’ neurocognition and biology under communicative pressures. Thus, language is the product of a multiscale communicative niche construction process at the intersection of biology, environment, and culture.