Christoph Reich is a former Research Intern in the Integrated Systems department at NEC Laboratories America, Inc., while studying at Technische Universitat Darmstadt.

Posts

A Perspective on Deep Vision Performance with Standard Image and Video Codecs

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Resource-constrained hardware such as edge devices or cell phones often rely on cloud servers to provide the required computational resources for inference in deep vision models. However transferring image and video data from an edge or mobile device to a cloud server requires coding to deal with network constraints. The use of standardized codecs such as JPEG or H.264 is prevalent and required to ensure interoperability. This paper aims to examine the implications of employing standardized codecs within deep vision pipelines. We find that using JPEG and H.264 coding significantly deteriorates the accuracy across a broad range of vision tasks and models. For instance strong compression rates reduce semantic segmentation accuracy by more than 80% in mIoU. In contrast to previous findings our analysis extends beyond image and action classification to localization and dense prediction tasks thus providing a more comprehensive perspective.

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Deep Video Codec Control for Vision Models

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Standardized lossy video coding is at the core of almost all real-world video processing pipelines. Rate control is used to enable standard codecs to adapt to different network bandwidth conditions or storage constraints. However standard video codecs (e.g. H.264) and their rate control modules aim to minimize video distortion w.r.t. human quality assessment. We demonstrate empirically that standard-coded videos vastly deteriorate the performance of deep vision models. To overcome the deterioration of vision performance this paper presents the first end-to-end learnable deep video codec control that considers both bandwidth constraints and downstream deep vision performance while adhering to existing standardization. We demonstrate that our approach better preserves downstream deep vision performance than traditional standard video coding.

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Differentiable JPEG: The Devil is in The Details

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JPEG remains one of the most widespread lossy image coding methods. However, the non-differentiable nature of JPEG restricts the application in deep learning pipelines. Several differentiable approximations of JPEG have recently been proposed to address this issue. This paper conducts a comprehensive review of existing diff. JPEG approaches and identifies critical details that have been missed by previous methods. To this end, we propose a novel diff. JPEG approach, overcoming previous limitations. Our approach is differentiable w.r.t. the input image, the JPEG quality, the quantization tables, and the color conversion parameters. We evaluate the forward and backward performance of our diff. JPEG approach against existing methods. Additionally, extensive ablations are performed to evaluate crucial design choices. Our proposed diff. JPEG resembles the (non-diff.) reference implementation best, significantly surpassing the recent-best diff. approach by 3.47dB (PSNR) on average. For strong compression rates, we can even improve PSNR by 9.51dB. Strong adversarial attack results are yielded by our diff. JPEG, demonstrating the effective gradient approximation. Our code is available at https://github.com/necla-ml/Diff-JPEG.

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Deep Video Codec Control

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Deep Video Codec Control Lossy video compression is commonly used when transmitting and storing video data. Unified video codecs (e.g., H.264 or H.265) remain the emph(Unknown sysvar: (de facto)) standard, despite the availability of advanced (neural) compression approaches. Transmitting videos in the face of dynamic network bandwidth conditions requires video codecs to adapt to vastly different compression strengths. Rate control modules augment the codec’s compression such that bandwidth constraints are satisfied and video distortion is minimized. While, both standard video codes and their rate control modules are developed to minimize video distortion w.r.t. human quality assessment, preserving the downstream performance of deep vision models is not considered. In this paper, we present the first end-to-end learnable deep video codec control considering both bandwidth constraints and downstream vision performance, while not breaking existing standardization. We demonstrate for two common vision tasks (semantic segmentation and optical flow estimation) and on two different datasets that our deep codec control better preserves downstream performance than using 2-pass average bit rate control while meeting dynamic bandwidth constraints and adhering to standardizations.

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