The trade-offs between compression performance and encoding complexity are key in software video encoding, even more so with increasing pressure on sustainability. Previous work "Towards much better SVT-AV1 quality-cycles tradeoffs for VOD applications" [1] described three approaches of evaluating compression efficiency vs cycles trade-offs within a convex-hull framework using the Dynamic Optimizer (DO) algorithm developed in [2] [3] for VOD applications. In parallel, the new video codec enhancer LCEVC (Low Complexity Enhancement Video Coding) [4], designed to provide gains in speed-quality trade-offs, has recently been standardized as MPEG-5 Part 2. The core idea of LCEVC is to use any video coding standard (such as AV1) as a base encoder at a lower resolution, and then reduce artifacts and reconstruct a full resolution output by combining the decoded low-resolution output with up to two low-complexity reconstruction enhancement sub-layers of the residual data. This paper starts by applying LCEVC to SVT-AV1 [5], as well as x264 [6] and x265 [7], while using two of the approaches presented in [1] to evaluate the resulting compression efficiency vs cycles trade-offs. The paper then discusses the benefits of LCEVC towards higher playback speed and lower battery power consumption when using AV1 software decoding. Results show that, with fast-encoding parameter selection using the discrete convex hull methodology, LCEVC improves the quality-cycles trade-offs for all the tested codecs and across the full complexity range. In the case of SVT-AV1, LCEVC yields a ~40% reduction in computations while achieving the same quality levels according to VMAF_NEG [8]. LCEVC also enlarges the set of mobile devices capable of playing HD as well as high-frame-rate content encoded with AV1 and extends mobile battery life by up to 50% with respect to state-of-the-art AV1 software decoding.

Software video encoders that have been developed based on the AVC, HEVC, VP9, and AV1 video coding standards have provided improved compression efficiency but at the cost of large increases in encoding complexity. As a result, there is currently no software video encoder that provides competitive quality-cycles tradeoffs extending from the AV1 high-quality range to the AVC low-complexity range. This paper describes methods based on the dynamic optimizer (DO) approach to further improve the SVT-AV1 overall quality-cycles tradeoffs for high-latency Video on Demand (VOD) applications. First the performance of the SVT-AV1 encoder is evaluated using the conventional DO approach, and then using the combined DO approach that accounts for all the encodings being considered in the selection of the encoding parameters. A fast parameter selection approach is then discussed. The latter allow for up to a 10x reduction in the complexity of the combined DO approach with minimal BD-rate loss.

The Scalable Video Technology AV1 (SVT-AV1) encoder is an open-source software AV1 encoder that is architected to yield excellent quality-speed-latency tradeoffs on CPU platforms for a wide range of video coding applications. The SVTAV1 encoder is based on the SVT architecture, which supports multi-dimensional parallelism, multi-pass partitioning decision, multi-stage/multi-class mode decision, and multi-level spatiotemporal prediction and residual coding algorithms. Given a latency constraint, the SVT-AV1 encoder maximizes the CPU utilization on multicore CPUs, through picturebased parallelism for high-latency video applications, and through segment-based parallelism for low-latency video applications. The picture-level/segment-level parallelism allows the SVT-AV1 encoder to produce identical bit streams, irrespective of whether single- or multi-threaded encoding is performed. In mode decision, the SVT-AV1 encoder yields many speed-quality tradeoffs with high granularity, mainly through multi-pass processing of each superblock and multistage/ multi-class processing of each block, and through the different levels of the prediction/coding features. The resulting speed-quality tradeoffs of SVT-AV1 are compared, in Video-On-Demand (VOD) use-cases, to those of libaom (another open-source AV1 encoder), and to those of the latest x264 (AVC), x265 (HEVC) and libvpx (VP9) open source encoders.

This paper presents the results of an informal subjective quality comparison between the current state of the emerging High Efficiency Video Coding (HEVC) draft standard and the well-established H.264 / MPEG-4 AVC High Profile (HP) for low-delay applications. The tests consisted of two basic encoding comparisons. First, we compare the Main profile low-delay configuration of the HEVC reference software (HM) against a similarly configured H.264 / MPEG-4 AVC HP reference encoder (JM). Additionally, to complement these results, the widely-recognized production-quality H.264 / MPEG-4 AVC encoder known as x264 is compared with a production-quality HEVC implementation from eBrisk Video. The encoding configurations are designed to reflect relevant application scenarios and to enable a fair comparison to the maximum extent feasible. When viewing HM and JM encoded video side-by-side in which the JM was configured to use approximately twice the bit rate of the HM encoded video, viewers indicated that they preferred the HM encoded video in approximately 74% of trials. Similarly, when comparing the eBrisk HEVC and x264 H.264 / MPEG-4 AVC production encoders in which x264 was configured to use approximately twice the bit rate of the eBrisk encoded video, viewers indicated they preferred the eBrisk HEVC encoded video in approximately 62% of trials. The selection of which encoding was displayed on which side for the side-by-side viewing was established in a randomized manner, and the subjective viewing experiments were administered in a double-blind fashion. The results reported in this paper generally confirm that the HEVC design (as represented by HM version 7.1 and separately by a production-quality HEVC implementation) exhibits a substantial improvement in compression capability beyond that of H.264 / MPEG-4 AVC (as represented by a similarly-configured JM version 18.3 and x264 version core 122 r2184, respectively) for low-delay video applications, with HEVC exhibiting roughly twice or more of the overall compression capability of H.264 / MPEG-4 AVC.

The high coding efficiency enabled by the H.264 standard comes with substantially greater algorithmic complexity as compared to that of existing standards. This additional complexity complicates very much the implementation and optimization tasks. However, efficient implementations on different platforms exist that achieve real-time constraints in several video applications. One such good example is UB Video's implementation of an H.264 Baseline encoder on the Texas Instruments' TMS320DM642 (DM642) Digital Signal Processor, which can achieve good speed-quality trade-offs in video conferencing applications. In this work, we analyze and optimize UB Video's implementation of its H. 264 Baseline encoder on the DM642. As a result, we have been able to improve memory transfer efficiency and reduce execution time, reducing the total number of encoder cycles by 18% without any loss in video quality.

Some of the major objectives of the JPEG 2000 still image coding standard were compression and memory efficiency, lossy to lossless coding, support for continuous-tone to bi-level images, error resilience, and random access to regions of interest. This paper will provide readers with some insight on various features and functionalities supported by a baseline JPEG 2000-compliant codec. Three JPEG 2000 software implementations (Kakadu, JasPer, JJ2000) are compared with several other codecs, including JPEG, JBIG, JPEG-LS, MPEG-4 VTC and H.264 intra coding. This study can serve as a guideline for users to estimate the effectiveness of JPEG 2000 for various applications, and to select optimal parameters according to specific application requirements.

The increasing availability of object-based video content requires new technologies for automatically extracting and matching of the low level features of arbitrarily shaped video. This paper proposes methods for shape retrieval of arbitrarily shaped video objects. Our methods take into account not only the still shape features but also the shape deformations that may occur in an object's lifespan. We compute the shape similarity of video objects by comparing the similarity of their representative temporal instances. We also describe motion of a video object via describing the deformations in an object's shape. Experimental results show that our proposed methods offer very good retrieval performance and match closely with the human ranking.