Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2204.05580

Codec Compression Efficiency Evaluation of MPEG-5 part 2 (LCEVC) using Objective and Subjective Quality Assessment

Nabajeet Barman·Steven Schmidt·Saman Zadtootaghaj·Maria G Martini
View original source ↗Read source document ↗Export metadata ↓

Abstract

With the increasing advancements in video compression efficiency achieved by newer codecs such as HEVC, AV1, and VVC, and intelligent encoding strategies, as well as improved bandwidth availability,there has been a proliferation and acceptance of newer services such as Netflix, Twitch, etc. However, such higher compression efficiencies are achieved at the cost of higher complexity and encoding delay, while many applications are delay sensitive. Hence, there is a requirement for faster, more efficient codecs to achieve higher encoding efficiency without significant trade-off in terms of both complexity and speed. We present in this work an evaluation of the latest MPEG-5 Part 2 Low Complexity Enhancement Video Coding (LCEVC) for live gaming video streaming applications. The results are presented in terms of bitrate savings using both subjective and objective quality measures as well as a comparison of the encoding speeds. Our results indicate that, for the encoding settings used in this work, LCEVC outperforms both x264 and x265 codecs in terms of bitrate savings using VMAF by approximately 42\% and 38\%. Using subjective results, it is found that LCEVC outperforms the respective base codecs, especially for low bitrates. This effect is more evident for x264 than for x265, i.e., for the latter the absolute improvement of quality scores is smaller. The objective and subjective results as well as sample video sequences are made available as part of an open dataset, LCEVC-LiveGaming at https://github.com/NabajeetBarman/LCEVC-LiveGaming.

Explore related subjects

cs.MM

Keep this discovery

BibTeXRIS

Nabajeet Barman, Steven Schmidt, Saman Zadtootaghaj, Maria G Martini. 2022-04-12. Codec Compression Efficiency Evaluation of MPEG-5 part 2 (LCEVC) using Objective and Subjective Quality Assessment. https://arxiv.org/abs/2204.05580

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Multi-Faceted Evaluation and Mitigation of Emotion Hallucinations in MLLMs

Multimodal large language models (MLLMs) have shown strong potential in open-ended emotion understanding, yet they often generate emotion hallucinations. Evaluating such hallucinations is particularly challenging for two reasons. First, emotion understanding spans multiple cognitive facets, from multimodal perception to psychological reasoning. Second, emotional interpretations are expressed in free-form language, making existing closed-ended protocols insufficient for evaluation. To address these challenges, we introduce EHR (Emotion Hallucination Rate), an evaluator that quantifies emotion hallucinations across six facets: expression, action, audio, instinct, logic, and conclusion. Using EHR, we reveal that existing mitigation methods often reduce hallucinations in some facets while aggravating them in others, exposing the limitation of coarse-grained correction and the need for facet-aware localization and mitigation. Motivated by this finding, we propose HMER (Hallucination-aware Memory-guided Emotion Reasoning), a training-free framework for emotion hallucination mitigation. HMER maintains a Hallucination Memory that records localized hallucinated claims and enables targeted logit rectification, together with an Anchor Memory that preserves reliable intermediate reasoning states to stabilize subsequent generation. By selectively suppressing unreliable cues while preserving trustworthy reasoning context, HMER enables fine-grained mitigation across diverse hallucination facets. Extensive experiments on 19 MLLMs demonstrate the prevalence of emotion hallucinations and the effectiveness of our framework across diverse model architectures.

cs.MM↗
arXiv2026

Multimodal Temporal Modeling for Continuous Group Emotion Recognition in Multi-party Dialogues

To realize natural behavior in dialogue agents in multi-party dialogue scenarios, it is important to understand group emotion such as valence and arousal as a whole. Most prior work addressed this task at the utterance level or using a coarse-grained time window, which is not sufficient to capture emotional dynamics. In this study, we formulate continuous recognition of the Group Emotion at a one-second resolution. Moreover, we also introduce the Mixed state, which captures the emotional divergence among participants in the group. We constructed a dataset with frame-level soft labels based on the TEIDAN corpus and propose a multimodal temporal framework that integrates audio and video information using a sliding-window context. Experimental results demonstrate that the temporal Transformer outperforms simple baselines and shows stronger temporal agreement with the ground-truth labels than the LLM-based model. The effect of context length is limited, whereas audio-visual input outperforms either unimodal input on the continuous-label metrics. Additionally, our analysis shows larger Group Emotion recognition errors in intervals with high Mixed values, exposing emotional divergence as a key challenge for group emotion recognition.

cs.MM↗
arXiv2026

MotionCanvas: Learning Implicit Motion Planning from Composable Kinematic Cues

Professional character animation requires both natural motion and precise, versatile control. For example, it is common for the creators to define the timing of a specified action, to control the motion range of the character's arm swing, and the route the character walks through, like specifying various kinematic motion cues on a ``motion canvas''. This motivates us to propose MotionCanvas, a model that supports \emph{cue-conditioned implicit motion planning} to faithfully and coherently connect all cues, dense or sparse, full or partial, into one full-body motion sequence. Specifically, MotionCanvas represents heterogeneous kinematic cues on a shared motion canvas, where position and rotation values are specified across body joints and time. A shared flow-matching model generates motion conditioned on this canvas, with optional language and input motion; cue imputation keeps the specified canvas values fixed in both training and sampling. To learn coherent completion across different cue sets, we train with a compositional cue sampler that varies when cues are applied, which positions or rotations are specified, and how they are combined. Together, these designs enable a single generator to synthesize globally coherent actions that jointly satisfy compatible heterogeneous cues. We test this planning ability with temporal, root, and body-part cues---alone and in combination---and language-guided editing. We naturally extend this evaluation to sequential generation and motion repair, since both require the same ability to organize coherent motion from kinematic cues. Across these evaluations, MotionCanvas establishes state-of-the-art results in controlled-motion quality, mixed-cue adherence, sequential generation, instruction editing, and motion repair while preserving its text-to-motion capability.

cs.MM↗