Encoding Time Research Articles

Usefulness of Pointwise Encoding Time Reduction with Radial Acquisition and Subtraction-Based Magnetic Resonance Angiography after Cerebral Aneurysm Clipping

ObjectiveTime-of-flight magnetic resonance angiography (MRA) is limited by clip-induced artifacts after cerebral aneurysmal clipping. Recently, ultrashort echo time was shown to reduce metal artifacts. We assessed the pointwise encoding time reduction with radial acquisition (PETRA) sequence in subtraction-based MRA as an ultrashort echo time method during follow-up for clipping surgery.MethodsWe retrospectively evaluated 114 branches of 63 aneurysms in 56 patients treated with titanium clips using MRA and 3-dimensional computed tomography angiography. The appearance using each method was compared, and the associations between visibility on PETRA-MRA, clip number and shape, and amount of hematoma were examined. Furthermore, the visibility of the aneurysm remnants and 2 clipping cases with cobalt-chromium-nickel-molybdenum clips were evaluated.ResultsNo branches were visible using time-of-flight-MRA, but 79 of 114 branches (69.3%) were visible on PETRA-MRA. PETRA-MRA was effective for follow-up imaging in 33 of 63 aneurysms (52.4%). The median vessel diameters were 1.67 mm (interquartile range, 1.24–2.62 mm) and 0.96 mm (interquartile range, 0.59–1.53 mm) in the visible and invisible groups, respectively. Only the vessel diameter correlated significantly (P < 0.001) with the visibility on PETRA-MRA. A receiver operating characteristic curve for the association between the vessel diameter and visibility on PETRA-MRA showed a cutoff value of 1.26 mm for vessel diameter. Cobalt-chromium-nickel-molybdenum clips produced a strong artifact, even on PETRA-MRA. All 4 residual aneurysms were visible on PETRA-MRA.ConclusionsPETRA-MRA can be useful for follow-up aneurysm imaging when the diameter of vessels adjacent to the clip exceeds 1.26 mm. However, its usefulness is limited to titanium clips.

The effect of categories on relative encoding biases in memory-based judgments

People are very good at making relative judgments (determining where a stimulus ranks in a distribution) but not very good at making absolute judgments (determining the absolute properties of the stimulus independent of context). Thus, when evaluating various stimuli, people tend to encode ordinal rank rather than objective quality. This leads to a robust bias in memory-based judgments: when the nature of the distribution shifts, people fail to update their memories of ordinal rank, and thus incorrectly judge a stimulus that is at the top [bottom] of the distribution at Time 1 to also be at the top [bottom] of a different distribution at Time 2. In two studies, we demonstrate a crucial moderator of the relative encoding bias on memory-based judgments: category saliency at the time of encoding. More specifically, we reveal that this memory-based judgment bias is reduced if it is made salient at the time of encoding that the stimuli from Time 1 and Time 2 are from different categories and thus different distributions.

Visual memory benefits from prolonged encoding time regardless of stimulus type.

It is generally assumed that the storage capacity of visual working memory (VWM) is limited, holding about 3-4 items. Recent work with real-world objects, however, has challenged this view by providing evidence that the VWM capacity for real-world objects is not fixed but instead increases with prolonged encoding time (Brady, Störmer, & Alvarez, 2016). Critically, in this study, no increase with prolonged encoding time was observed for storing simple colors. Brady et al. (2016) argued that the larger capacity for real-world objects relative to colors is due to the additional conceptual information of real-world objects. With basically the same methods of Brady et al., in Experiments 1-3, we were unable to replicate their basic findings. Instead, we found that visual memory for simple colors also benefited from prolonged encoding time. Experiment 4 showed that the scale of the encoding time benefit was the same for familiar and unfamiliar objects, suggesting that the added conceptual information does not contribute to this benefit. We conclude that visual memory benefits from prolonged encoding time regardless of stimulus type. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Prefrontal Neural Ensembles Develop Selective Code for Stimulus Associations within Minutes of Novel Experiences.

Prevailing theories posit that the hippocampus rapidly learns stimulus conjunctions during novel experiences, whereas the neocortex learns slowly through subsequent, off-line interaction with the hippocampus. Parallel evidence, however, shows that the medial prefrontal cortex (mPFC; a critical node of the neocortical network supporting long-term memory storage) undergoes rapid modifications of gene expression, synaptic structure, and physiology at the time of encoding. These observations, along with impaired learning with disrupted mPFC, suggest that mPFC neurons may exhibit rapid neural plasticity during novel experiences; however, direct empirical evidence is lacking. We extracellularly recorded action potentials of cells in the prelimbic region of the mPFC, while male rats received a sequence of stimulus presentations for the first time in life. Moment-to-moment tracking of neural ensemble firing patterns revealed that the prelimbic network activity exhibited an abrupt transition within 1 min after the first encounter of an aversive but not neutral stimulus. This network-level change was driven by ∼15% of neurons that immediately elevated their spontaneous firing rates (FRs) and developed firing responses to a neutral stimulus preceding the aversive stimulus within a few instances of their pairings. When a new sensory stimulus was paired with the same aversive stimulus, about half of these neurons generalized firing responses to the new stimulus association. Thus, prelimbic neurons are capable of rapidly forming ensemble codes for novel stimulus associations within minutes. This circuit property may enable the mPFC to rapidly detect and selectively encode the central content of novel experiences.SIGNIFICANCE STATEMENT During a new experience, a region of the brain, called the hippocampus, rapidly forms its memory and later instructs another region, called the neocortex, that stores its content. Consistent with this dominant view, cells in the neocortex gradually strengthen the selectivity for the memory content over weeks after novel experiences. However, we still do not know precisely when these cells begin to develop the selectivity. We found that neocortical cells were capable of forming the selectivity for ongoing events within a few minutes of new experiences. This finding provides support for an alternative view that the neocortex works with, but not follows, the hippocampus to form new memories.

Complexity reduction for HEVC encoder using one-dimensional filtering based constrained one-bit transform

This paper presents a one-dimensional-filtering-based constrained one-bit transformation (1D-C1BT)-based motion estimation to reduce the complexity of the High Efficiency Video Coding (HEVC) encoder. The proposed 1D-C1BT requires integer arithmetic additions, subtraction and shift operations to convert full resolution video frames to two bit depth 1D-C1BT frames. By embedding the 1D-C1BT in the HEVC encoder, the motion estimation (ME) complexity of the HEVC encoder is greatly reduced in terms of Motion Estimation Time (MET) and total Encoding Time (ET) in uni-prediction and bi-prediction respectively. From the experimental results, it is shown that the 1D-C1BT accompanying full search (FS) and test zonal search (TZS) algorithms greatly reduces MET and ET as compared to the FS and TZS algorithms of the HEVC encoder respectively. To further reduce the intricacy of the HEVC encoder in the fast search mode, star-diamond (SD) search accompanying 1D-C1BT is proposed. The 1D-C1BT accompanying SD further reduces MET and ET with small increment in Bjontegaard Delta Rate and small decrement in Bjontegaard Delta Peak Signal to Noise Ratio, compared to the TZS algorithm of the HEVC encoder. In the FS mode of the HEVC encoder, the proposed 1D-C1BT, accompanying FS, reduces MET by 86.00% and ET by 83.94%, with BD-Rate of 0.1452% and BD-PSNR of 0.2635% in low delay-P main profile. The proposed ME algorithms, 1D-C1BT accompanying full search (FS + 1D-C1BT), 1D-C1BT accompanying test zonal search (TZS + 1D-C1BT) and 1D-C1BT accompanying star diamond search (SD + 1D-C1BT) greatly reduce ME complexity in all the three configurations (low delay-P, low delay-B and random access main profile) of the HEVC encoder.

MRI phase offset correction method impacts quantitative susceptibility mapping

Individual channel ultra-high field (7T) phase images have to be phase offset corrected prior to the mapping of magnetic susceptibility of tissue. Whilst numerous methods have been proposed for gradient recalled echo MRI phase offset correction, it remains unclear how they affect quantitative magnetic susceptibility values derived from phase images. Methods already proposed either employ a single or multiple echo time MRI data. In terms of the latter, offsets can be derived using an ultra-short echo time acquisition, or by estimating the offset based on two echo points with the assumption of linear phase evolution with echo time. Our evaluation involved 32 channel multi-echo time 7T GRE (Gradient Recalled Echo) and ultra-short echo time PETRA (Pointwise Encoding Time Reduction with Radial Acquisition) MRI data collected for a susceptibility phantom and three human brains. The combined phase images generated using four established offset correction methods (two single and two multiple echo time) were analysed, followed by an assessment of quantitative susceptibility values obtained for a phantom and human brains. The effectiveness of each method in removing the offsets was shown to reduce with increased echo time, decreased signal intensity and reduced overlap in coil sensitivity profiles. Quantitative susceptibility values and how they change with echo time were found to be method specific. Phase offset correction methods based on single echo time data have a tendency to produce more accurate and less noisy quantitative susceptibility maps in comparison with methods employing multiple echo time data.

Cloud-based elastic architecture for distributed video encoding: Evaluating H.265, VP9, and AV1

Areas with social and business impact such as entertainment, healthcare, surveillance, and e-learning would benefit from improvements in video coding and transcoding services. New codecs, such as AV1, are being developed to deal with new demands for high video resolutions with bandwidth constraints and quality requirements. However, these new codecs have high computational requirements and new strategies are needed to accelerate their processing.Cloud computing offers interesting features such as on-demand resource allocation, multitenancy, elasticity, and resiliency among others. Deploying video coding and transcoding services on these infrastructures is suitable because it allows the adaptation of the resources to the workload, offers high availability, and provides ubiquitous access.This work proposes a cloud-based distributed architecture, tuned for video coding, that relies on an elastic pool of workers and media servers to provide fault-tolerance. A distributed application is deployed on the top of the architecture to split the video encoding process of a video in several jobs that can be dynamically assigned among the elastic pool of workers. The proposed solution is analyzed in terms of scalability, resource usage, and job distribution varying the number of workers for three well-known video codecs: H.265, VP9, and AV1. Moreover, the quality of the encoded videos has been evaluated for different bit rates and number of frames per job using full reference metrics like: PSNR, MS-SSIM, and VIF. Results show that our solution achieve similar quality and bitrate compared with full video coding while improving the total encoding time, which can decrease more than 90% depending on the encoder and the number of workers.

Visuo-Perceptual and Decision-Making Contributions to Visual Hallucinations in Mild Cognitive Impairment in Lewy Body Disease: Insights from a Drift Diffusion Analysis.

Background: Visual hallucinations (VH) are a common symptom in dementia with Lewy bodies (DLB); however, their cognitive underpinnings remain unclear. Hallucinations have been related to cognitive slowing in DLB and may arise due to impaired sensory input, dysregulation in top-down influences over perception, or an imbalance between the two, resulting in false visual inferences. Methods: Here we employed a drift diffusion model yielding estimates of perceptual encoding time, decision threshold, and drift rate of evidence accumulation to (i) investigate the nature of DLB-related slowing of responses and (ii) their relationship to visuospatial performance and visual hallucinations. The EZ drift diffusion model was fitted to mean reaction time (RT), accuracy and RT variance from two-choice reaction time (CRT) tasks and data were compared between groups of mild cognitive impairment (MCI-LB) LB patients (n = 49) and healthy older adults (n = 25). Results: No difference was detected in drift rate between patients and controls, but MCI-LB patients showed slower non-decision times and boundary separation values than control participants. Furthermore, non-decision time was negatively correlated with visuospatial performance in MCI-LB, and score on visual hallucinations inventory. However, only boundary separation was related to clinical incidence of visual hallucinations. Conclusions: These results suggest that a primary impairment in perceptual encoding may contribute to the visuospatial performance, however a more cautious response strategy may be related to visual hallucinations in Lewy body disease. Interestingly, MCI-LB patients showed no impairment in information processing ability, suggesting that, when perceptual encoding was successful, patients were able to normally process information, potentially explaining the variability of hallucination incidence.

Adaptive CU Split Decision Based on Deep Learning and Multifeature Fusion for H.266/VVC

With the development of technology, the hardware requirement and expectations of user for visual enjoyment are getting higher and higher. The multitype tree (MTT) architecture is proposed by the Joint Video Experts Team (JVET). Therefore, it is necessary to determine not only coding unit (CU) depth but also its split mode in the H.266/Versatile Video Coding (H.266/VVC). Although H.266/VVC achieves significant coding performance on the basis of H.265/High Efficiency Video Coding (H.265/HEVC), it causes significantly coding complexity and increases coding time, where the most time-consuming part is traversal calculation rate-distortion (RD) of CU. To solve these problems, this paper proposes an adaptive CU split decision method based on deep learning and multifeature fusion. Firstly, we develop a texture classification model based on threshold to recognize complex and homogeneous CU. Secondly, if the complex CUs belong to edge CU, a Convolutional Neural Network (CNN) structure based on multifeature fusion is utilized to classify CU. Otherwise, an adaptive CNN structure is used to classify CUs. Finally, the division of CU is determined by the trained network and the parameters of CU. When the complex CUs are split, the above two CNN schemes can successfully process the training samples and terminate the rate-distortion optimization (RDO) calculation for some CUs. The experimental results indicate that the proposed method reduces the computational complexity and saves 39.39% encoding time, thereby achieving fast encoding in H.266/VVC.

Source memory for advertisements: The role of advertising message credibility

Advertising is seen as an untrustworthy source because of the perceived self-interest of the advertisers in presenting product information in a biased or misleading way. Regulations require advertising messages in print and online media to be labeled as advertisements to allow recipients to take source information into account when judging the credibility of the messages. To date, little is known about how these source tags are remembered. Research within the source-monitoring framework suggests that source attributions are not only based on veridical source memory but are often reconstructed through schematic guessing. In two experiments, we examined how the credibility of advertising messages affects these source attribution processes. The source of the messages affected judgments of credibility at the time of encoding, but the source tags were forgotten after a short period of time. Retrospective source attributions in the absence of memory for the source tags were strongly influenced by the a priori credibility of the messages: Statements with a low a priori credibility were more likely to be (mis)attributed to advertising than statements with high a priori credibility. These findings suggest that the mere labeling of untrustworthy sources is of limited use because source information is quickly forgotten and memory-based source attributions are strongly biased by schematic influences.

A novel fast intra mode decision for versatile video coding

AbstractThe latest video coding standard Versatile Video Coding (VVC) obtains superior coding efficiency compared to the High Efficiency Video Coding (HEVC), which is achieved by incorporating more effective and complex new coding tools. In this paper, we propose a novel fast intra mode decision algorithm for VVC, including following two strategies: (1) the correlation between the optimal modes of the adjacent blocks and the modes selected in the rough modes decision (RMD) process is analyzed and applied to reduce the modes in the candidate list; (2) modes in the candidate list are sorted in ascending order according to the modes’ cost calculated in the RMD process. An early termination method is proposed for terminating the optimal prediction mode decision process based on this new order early. These two strategies are incorporated into intra coding to reduce the coding complexity. Since these two strategies do not add any additional computational complexity, the proposed fast algorithm can achieve more complexity reduction. The experimental results show that the complexity reduction of the proposed algorithm is up to 44.74% compared to VVC reference software VTM2.0, and averagely 30.59% encoding time saving with 0.86% BDBR increase.

Fast intra-coding unit partition decision in H.266/FVC based on deep learning

In the recent Future Video Coding (FVC) standard developed by the Joint Video Exploration Team (JVET), the quad-tree binary-tree (QTBT) block partition module makes use of rectangular block forms and additional square block sizes compared to quad-tree (QT) block partitioning module proposed in the predecessor High-Efficiency Video Coding (HEVC) standard. This block flexibility, induced with the QTBT module, significantly improves compression performance while it dramatically increases coding complexity due to the brute force search for Rate Distortion Optimization (RDO). To cope with this issue, it is necessary to consider the unique characteristics of QTBT in FVC. In this paper, we propose a fast QT partitioning algorithm based on a deep convolutional neural network (CNN) model to predict coding unit (CU) partition instead of RDO which enhances considerably QTBT performance for intra-mode coding. Based on a suitable diversified CU partition patterns database, the optimization process is set up with three levels CNN structure developed to learn the split or non-split decision from the established database. Experimental results reveal that the proposed algorithm can accelerate the QTBT block partition structure by reducing the intra-mode encoding time by an average of 35% with a bit rate increase of 1.7%, allowing its application in practical scenarios.

An Improved Fast Affine Motion Estimation Based on Edge Detection Algorithm for VVC

As a newly proposed video coding standard, Versatile Video Coding (VVC) has adopted some revolutionary techniques compared to High Efficiency Video Coding (HEVC). The multiple-mode affine motion compensation (MM-AMC) adopted by VVC saves approximately 15%-25% Bjøntegaard Delta Bitrate (BD-BR), with an inevitable increase of encoding time. This paper gives an overview of both the 4-parameter affine motion model and the 6-parameter affine motion model, analyzes their performances, and proposes improved algorithms according to the symmetry of iterative gradient descent for fast affine motion estimation. Finally, the proposed algorithms and symmetric MM-AMC flame of VTM-7.0 are compared. The results show that the proposed algorithms save 6.65% total encoding time on average, which saves approximately 30% encoding time of affine motion compensation.

Subjective Evaluation of Ultra-high Definition (UHD) Videos

This paper presents a detailed subjective quality assessment for the ultra-high definition (UHD) videos having frame rates of 30fps and 60 fps. The subjective assessment is based on the ITU-R BT-500 recommendations, where double stimulus continuous quality scale (DSCQS-type II) test is performed for the evaluation of the perceived quality of the user’s in terms of differential mean opinion score (DMOS). Encoding of the UHD videos by opting encoders i.e. H.264/AVC, H.265/HEVC, and VP9 at five different quantization parameter (QP) levels is done to investigate the perceived user’s quality of experience (QoE) given as DMOS. Moreover, the encoding efficiency as the encoding time for each encoder and qualitative performance by employing full-reference (FR) quality metrics are presented in this work.

Fast prediction mode selection and CU partition for HEVC intra coding

Compared with the predecessor H.264/advanced video coding, high-efficiency video coding (HEVC) is a new video coding standard with nearly double coding efficiency under the same coding quality. However, the computing complexity of HEVC increases sharply. To solve this problem, a fast algorithm for intra prediction mode selection based on mode grouping is proposed by reducing the number of modes entering rough mode decision. Moreover, the dual support vector machine is proposed to efficiently select the coding unit (CU) size, which is based on texture features of CU and sub-CUs including content complexity and direction complexity. By using the new CU size selection algorithm, the encoder can confirm the split for complex CU and terminate the split for simple CU in advance, so as to reduce the computing complexity of CU size selection. The experimental results show that by employing the two fast algorithms in intra coding, it can save 42.80% encoding time, with 0.98% increment in bit rate and 0.018 dB loss of peak-signal-to-noise ratio of luminance, compared with the reference software x265-1.7.

Spiking activity in the human hippocampus prior to encoding predicts subsequent memory

Encoding activity in the medial temporal lobe, presumably evoked by the presentation of stimuli (postonset activity), is known to predict subsequent memory. However, several independent lines of research suggest that preonset activity also affects subsequent memory. We investigated the role of preonset and postonset single-unit and multiunit activity recorded from epilepsy patients as they completed a continuous recognition task. In this task, words were presented in a continuous series and eventually began to repeat. For each word, the patient's task was to decide whether it was novel or repeated. We found that preonset spiking activity in the hippocampus (when the word was novel) predicted subsequent memory (when the word was later repeated). Postonset activity during encoding also predicted subsequent memory, but was simply a continuation of preonset activity. The predictive effect of preonset spiking activity was much stronger in the hippocampus than in three other brain regions (amygdala, anterior cingulate, and prefrontal cortex). In addition, preonset and postonset activity around the encoding of novel words did not predict memory performance for novel words (i.e., correctly classifying the word as novel), and preonset and postonset activity around the time of retrieval did not predict memory performance for repeated words (i.e., correctly classifying the word as repeated). Thus, the only predictive effect was between preonset activity (along with its postonset continuation) at the time of encoding and subsequent memory. Taken together, these findings indicate that preonset hippocampal activity does not reflect general arousal/attention but instead reflects what we term "attention to encoding."

Temperature-Sensitive Frozen-Tissue Imaging for Cryoablation Monitoring Using STIR-UTE MRI.

The aim of this study was to develop a method to delineate the lethally frozen-tissue region (temperature less than -40°C) arising from interventional cryoablation procedures using a short tau inversion-recovery ultrashort echo-time (STIR-UTE) magnetic resonance (MR) imaging sequence. This method could serve as an intraprocedural validation of the completion of tumor ablation, reducing the number of local recurrences after cryoablation procedures. The method relies on the short T1 and T2* relaxation times of frozen soft tissue. Pointwise Encoding Time with Radial Acquisition, a 3-dimensional UTE sequence with TE = 70 microseconds, was optimized with STIR to null tissues with a T1 of approximately 271 milliseconds, the threshold T1. Because the T1 relaxation time of frozen tissue in the temperature range of -40°C < temperature < -8°C is shorter than the threshold T1 at the 3-tesla magnetic field, tissues in this range should appear hyperintense. The sequence was evaluated in ex vivo frozen tissue, where image intensity and actual tissue temperatures, measured by thermocouples, were correlated. Thereafter, the sequence was evaluated clinically in 12 MR-guided prostate cancer cryoablations, where MR-compatible cryoprobes were used to destroy cancerous tissue and preserve surrounding normal tissue. The ex vivo experiment using a bovine muscle demonstrated that STIR-UTE images showed regions approximately between -40°C and -8°C as hyperintense, with tissues at lower and higher temperatures appearing dark, making it possible to identify the region likely to be above the lethal temperature inside the frozen tissue. In the clinical cases, the STIR-UTE images showed a dark volume centered on the cryoprobe shaft, Vinner, where the temperature is likely below -40°C, surrounded by a doughnut-shaped hyperintense volume, where the temperature is likely between -40°C and -8°C. The hyperintense region was itself surrounded by a dark volume, where the temperature is likely above -8°C, permitting calculation of Vouter. The STIR-UTE frozen-tissue volumes, Vinner and Vouter, appeared significantly smaller than signal voids on turbo spin echo images (P < 1.0 × 10), which are currently used to quantify the frozen-tissue volume ("the iceball"). The ratios of the Vinner and Vouter volumes to the iceball were 0.92 ± 0.08 and 0.29 ± 0.07, respectively. In a single postablation follow-up case, a strong correlation was seen between Vinner and the necrotic volume. Short tau inversion-recovery ultrashort echo-time MR imaging successfully delineated the area approximately between -40°C and -8°C isotherms in the frozen tissue, demonstrating its potential to monitor the lethal ablation volume during MR-guided cryoablation.

Odor modulates the temporal dynamics of fear memory consolidation.

Systems consolidation (SC) theory proposes that recent, contextually rich memories are stored in the hippocampus (HPC). As these memories become remote, they are believed to rely more heavily on cortical structures within the prefrontal cortex (PFC), where they lose much of their contextual detail and become schematized. Odor is a particularly evocative cue for intense remote memory recall and despite these memories being remote, they are highly contextual. In instances such as posttraumatic stress disorder (PTSD), intense remote memory recall can occur years after trauma, which seemingly contradicts SC. We hypothesized that odor may shift the organization of salient or fearful memories such that when paired with an odor at the time of encoding, they are delayed in the de-contextualization process that occurs across time, and retrieval may still rely on the HPC, where memories are imbued with contextually rich information, even at remote time points. We investigated this by tagging odor- and non-odor-associated fear memories in male c57BL/6 mice and assessed recall and c-Fos expression in the dorsal CA1 (dCA1) and prelimbic cortex (PL) 1 or 21 d later. In support of SC, our data showed that recent memories were more dCA1-dependent whereas remote memories were more PL-dependent. However, we also found that odor influenced this temporal dynamic, biasing the memory system from the PL to the dCA1 when odor cues were present. Behaviorally, inhibiting the dCA1 with activity-dependent DREADDs had no effect on recall at 1 d and unexpectedly caused an increase in freezing at 21 d. Together, these findings demonstrate that odor can shift the organization of fear memories at the systems level.

A Fast Mode Decision Algorithm for Intra Prediction in High Efficiency Video Coding

High Efficiency Video Coding (HEVC) is the next generation video coding standard. In HEVC, 35 intra prediction modes are defined to improve coding efficiency, which result in huge computational complexity, as a large number of prediction modes and a flexible coding unit (CU) structure is adopted in CU coding. To reduce this computational burden, this paper presents a gradient-based candidate list clipping algorithm for Intra mode prediction. Experimental results show that the proposed algorithm can reduce 29.16% total encoding time with just 1.34% BD-rate increase and –0.07 dB decrease of BD-PSNR.

Maximum-Entropy-Model-Enabled Complexity Reduction Algorithm in Modern Video Coding Standards

Symmetry considerations play a key role in modern science, and any differentiable symmetry of the action of a physical system has a corresponding conservation law. Symmetry may be regarded as reduction of Entropy. This work focuses on reducing the computational complexity of modern video coding standards by using the maximum entropy principle. The high computational complexity of the coding unit (CU) size decision in modern video coding standards is a critical challenge for real-time applications. This problem is solved in a novel approach considering CU termination, skip, and normal decisions as three-class making problems. The maximum entropy model (MEM) is formulated to the CU size decision problem, which can optimize the conditional entropy; the improved iterative scaling (IIS) algorithm is used to solve this optimization problem. The classification features consist of the spatio-temporal information of the CU, including the rate–distortion (RD) cost, coded block flag (CBF), and depth. For the case analysis, the proposed method is based on High Efficiency Video Coding (H.265/HEVC) standards. The experimental results demonstrate that the proposed method can reduce the computational complexity of the H.265/HEVC encoder significantly. Compared with the H.265/HEVC reference model, the proposed method can reduce the average encoding time by 53.27% and 56.36% under low delay and random access configurations, while Bjontegaard Delta Bit Rates (BD-BRs) are 0.72% and 0.93% on average.