In DNA data storage, data is retrieved from an unordered pool of DNA strands, posing challenges for sequential access and necessitating strong forward error correction (FEC). We propose a progressive video playback framework that overcomes these challenges by (a) hierarchically chunking a fragmented MP4 (fMP4) video file at frame granularity, prioritizing key frames in the encoding, and (b) using a decay-weighted Raptor fountain code biased toward early frames. By decaying the coding priority over frame index, the decoder can recover initial frames with fewer reads, enabling near real-time playback of the video from DNA. We integrate an inner ReedSolomon (RS) code and sequence checksum to correct sequencing errors, so that the fountain decoder operates on an effective erasure channel. Simulation results demonstrate that our biased coding dramatically reduces recovery time for first frames, allowing the video frame rate to ramp up to 30 fps as sequencing continues. This work is the first to achieve progressive video decoding from DNA storage using Raptor codes, extending recent advances in progressive image decoding in this domain.
In this paper, we propose an intra prediction mode derivation method for beyond VVC. Our proposed method utilizes template matching-based block vectors to identify reference blocks with similar characteristics to the current block, aiming to derive more accurate prediction modes. Unlike conventional mode derivation method such as in enhanced compression model (ECM) that rely solely on neighboring samples, our approach leverages block-level similarities while avoiding additional signaling overhead. The key feature of our approach is two-fold: (1) utilizing multiple template matching candidates to identify similar reference blocks within the current frame, and (2) evaluating prediction modes using the neighboring samples of the current block for multiple reference blocks. By combining these two aspects, our method leverages block-level similarities while maintaining computational efficiency through the reuse of existing reference samples. Experimental results under JVET common test conditions show that the proposed method achieves 0.10 % bitrate reduction compared with ECM-14.
This paper reports on the process of composing a set of test sequences for evaluation of responses to the Call for Evidence on video compression with capability beyond VVC currently prepared in the Joint Video Experts Team of ISO/IEC JTC 1/SC 29 and ITU-T SG21. The goal is to compose a test set covering the intended application spaces with associated challenging target bitrate values. These values are selected such that candidate coding schemes can be evaluated without saturation effects in the low or high visual quality ranges. After pre-screening candidate sequences, a novel succession test method is applied to efficiently determine viable rate points with distinct quality levels. The outcome is evaluated using the Degradation Category Rating (DCR) method. The applied succession test method was found to be useful and efficient for assessing the draft rate point allocation for the test sequences. Some refinement of the method might be explored in future experiments.
Efficient inter prediction is fundamental to video coding, as it reduces temporal redundancy by leveraging information from neighboring frames. The quality of these reference frames critically impacts prediction accuracy. While numerous neural network-based methods have been proposed to enhance reference frames, most rely on convolutional architectures that excel at modeling local spatial features but struggle with complex motion due to their limited receptive fields. Improving their performance often demands higher model complexity, resulting in a suboptimal trade-off between performance and computational complexity. To address these limitations, we present the first exploration of a Mamba-based network that precisely captures spatiotemporal information with linear computational cost for inter prediction enhancement. Specifically, a Mamba-based video frame interpolation model is employed to synthesize a virtual reference frame using a pair of previously reconstructed frames selected from the decoded picture buffer as input. The synthesized frame is then inserted into the reference picture list and utilized during motion estimation and compensation. Moreover, we propose the QP-Progress training strategy to better align the interpolation network with the encoder’s operating conditions and improve robustness to compression artifacts, without sacrificing motion modeling accuracy. This strategy progressively incorporates higher-QP compressed frames during training while retaining low-QP samples to maintain the motion modeling capacity acquired in the early training stages. Experimental results show that our method achieves coding efficiency gains of 4.46%/4.12%/4.20% for Y/U/V components under random access configuration against NNVC-12.0 anchor. Compared with other existing approaches, our method achieves superior performance gain with significantly lower complexity.
This paper introduces a novel intra prediction method aimed at improving the coding efficiency of the Enhanced Compression Model (ECM). The proposed Occurrence-based Intra Coding (OBIC) method determines the optimal intra modes for block coding by analyzing the sample-wise occurrences of intra modes in the neighboring blocks. These occurrences are aggregated into a histogram of occurrences (HoC), from which the most frequent intra modes in the causal neighborhood are identified. The five most frequently-occurring modes’ predictions are then blended with planar prediction to predict the block. The OBIC method was introduced at the 33rd JVET meeting and adopted into the ECM software at the 34th meeting. The proposed method achieves average BD-Rate savings of 0.10% for the All-Intra (AI) configuration and 0.05% for the Random Access (RA) configuration compared to version 12.0 of the ECM software.
