For decades, the Bjøntegaard Delta (BD) has been the metric for evaluating codec Rate-Distortion (R-D) performance. Yet, in most studies, BD is determined using just 4–5 R-D data points, could this be sufficient? As codecs and quality metrics advance, does the conventional BD estimation still hold up? Crucially, are the performance improvements of new codecs and tools genuine, or merely artifacts of estimation flaws? We address these concerns by reevaluating BD estimation. We have established a large-scale, high-precision R-D dataset to verify the accuracy of existing BD estimation algorithms. Moreover, we propose a robust method for high-precision BD estimation across diverse compression scenarios, enhanced by a reliability assessment to determine the probability distribution of BD values from R-D sample points. This approach both assesses the reliability of BD calculations and serves as a precise BD estimator. Our method's validity is confirmed through extensive testing on a dataset we constructed. Our findings advocate for the adoption of rigorous R-D sampling and reliability metrics in future compression research to ensure the validity and reliability of results. Our code and additional experimental details are publicly accessible at https://github.com/fgvfgfg564/BDCI.

Learned Image Compression (LIC) has experienced rapid growth with the emergence of diverse frameworks. However, the variability in model design and training datasets poses a challenge for the universal application of a single coding model. To address this problem, this paper introduces a pioneering multi-model image coding framework that integrates various image codecs to overcome these limitations. By dynamically allocating codecs to different image regions, our framework optimizes reconstruction quality within the constraints of limited bitrate and decoding time, offering a high-performance, ubiquitous solution for the rate-distortion-complexity trade-off. Our framework features a detailed codec assignment algorithm based on the Simulated Annealing (SA) method, selected for its proven efficacy in managing the discrete and intricate nature of codec assignment optimization. We have implemented a coarse-to-fine strategy, which significantly enhances efficiency. Notably, our framework maintains compatibility with all standard image codecs without necessitating structural modifications. Empirical results indicate that our framework establishes a new standard in LIC, advancing the Pareto frontier for performance-complexity trade-offs. It achieves a significant 70% reduction in decoding time compared to current state-of-the-art methods, without compromising reconstruction quality. Furthermore, under comparable conditions, our approach not only outperforms but significantly eclipses existing Rate-Distortion-Complexity (RDC) optimized codecs, with decoding speeds up to 30 times faster.

This paper presents an innovative pipeline design for the quantization of neural image codecs, ensuring robust and consistent deployment across various platform inferences. Our approach addresses a significant challenge prevalent in neural image compression networks: the lack of quantization methods for entropy probability hyper-priors, such as Gaussian mixture models (GMMs), in the existing literature. Our method serves as a solution to prevent decoding errors stemming from float-point discrepancies between the encoder and decoder, especially when executed on different platforms. By implementing a quantization scheme based on look-up tables (LUTs), we achieve precise quantization of GMM probability models with minimal performance degradation, approaching near losslessness. Moreover, our contribution offers a comprehensive quantization pipeline that facilitates dependable data transmission and harmonious synchronization of arithmetic coding algorithms between the encoder and decoder components. Empirical evidence highlights the effectiveness of our method, demonstrating seamless codec matching on diverse platforms and substantiating its capacity to enhance the integrity and security of compressed data. This work enhances the practical applicability of neural compression techniques across real-world scenarios.

Hardware-oriented neural video coding (NVC) has been an emerging research topic recently. In this work, we present a P-frame based NVC system by leveraging the parallel neural inference capability of resource-constrained embed platforms, such as field programmable gate array (FPGA). The proposed system, namely FPX-NVC, consists of multiple resolution video capture, reconfigurable and integerized neural video codec and display. Our neural codec enjoys the learned unidirectional inter-predictive coding by jointly optimizing the implicit motion representation (IMR), feature domain motion compensation, and the corresponding prediction error compression, realizing end-to-end on-board encoding and decoding. Our system supports end-to-end video data operation flow from capturing to neural coding and display. To our knowledge, the FPX-NVC is the first inter-predictive coding (P-frame) based hardware NVC system.

The recent trend in neural image compression (NIC) research could be generally grounded into two categories: analysis-synthesis transform network improvements and entropy estimation optimization. They promote the compression efficiency of NIC by leveraging more expressive network structures and advanced entropy models respectively. From a different but more systematic viewpoint, we extend the horizon of NIC from software- to hardware-based lossy compression using more resource-constrained platforms, such as field programmable gate array (FPGA) or deep-learning processor unit (DPU). In this paper, we propose a novel hardware-oriented NIC system for real-time edge-computing video services. We for the first time present FPX-NIC, an FPGA-accelerated NIC framework designed for hardware encoding, which consists of a novel NIC scheme and an energy-efficient neural network (NN) deployment method. The former contribution is a block-based adaptive NIC approach based on local content characteristics. Essential side-information is also signalled to realize adaptive patch representation. The critical advantage of our latter contribution lies in the network-reconfigurable framework plus fixed-precision weights quantization method that takes advantage of quantization-aware post training procedure to compensate the performance degradation caused by quantization error. Therefore it is able to improve both processing speed and energy efficiency. We finally establish an intelligent video coding system using the proposed scheme, enabling visual capturing, neural encoding, decoding, and display, realizing 4K ultra-high-definition (UHD) all intra neural video coding on edge-computing devices.