Context adaptive thresholding and entropy coding for very low complexity JPEG transcoding

Xu, Xing; Zahaib, Aichta; Govindan, Ramesh; Lloyd, Wyatt; Ortega, Antonio

doi:10.1109/icassp.2016.7471905

Cited by 3 publications

(5 citation statements)

References 14 publications

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“…That Web server then sends the photo to a transcoder machine in the transcoding tier (2). The transcoder then sends the photo to the backend storage tier (3), and, once the photo is safely stored, acknowledgements flow in the reverse direction (4)(5)(6).…”

Section: Integrating Recompression Into Photomentioning

confidence: 99%

“…Users download photos from the first on-path cache they encounter with the photo: the device cache (1), the edge cache that handles their request (1-2-8), or the origin cache (1-2-3-7-8). If the photo is not in one of those caches it is fetched from the backend storage system via a transcoder (1)(2)(3)(4)(5)(6)(7)(8).…”

Section: Papermentioning

confidence: 99%

“…As opposed to traditional sender/receiver compression scheme where sender encodes the image and the receiver decodes it, the large-scale photo sharing services represent a new compression setting, with some special properties that offer opportunities for better compression. 5 Property 1: Collocated Encoder/Decoder. Instead of having two distinct sender/receiver entities where the encoder and the decoder are in physically separate locations, the recompression setting in photo-sharing service includes both compression and decompression within the service.…”

Section: A Why a New Recompression Strategy?mentioning

confidence: 99%

“…L-ROMP amplifies the compression gain of ROMP, adds no addition complexity to decompression, and is perceptually nearly lossless. ROMP and L-ROMP have been published in [5]. This paper includes more results for evaluation of ROMP and L-ROMP.…”

Section: Introductionmentioning

confidence: 99%

“…[5] presents the compression ratio results of Figures7a and 7b(but not complexity results), and complexity results of Figure9afor quality parameter 75 only. These are the only overlap in results presented in the two papers.2 JPEG Optimized is a lossless compression technique for JPEG Standard format, which provides additional compression with negligible overhead.…”

mentioning

confidence: 99%

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Reducing Storage in Large-Scale Photo Sharing Services using Recompression

Xu¹,

Akhtar²,

Lloyd³

et al. 2019

Preprint

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The popularity of photo sharing services has increased dramatically in recent years. Increases in users, quantity of photos, and quality/resolution of photos combined with the user expectation that photos are reliably stored indefinitely creates a growing burden on the storage backend of these services. We identify a new opportunity for storage savings with application-specific compression for photo sharing services: photo recompression.We explore new photo storage management techniques that are fast so they do not adversely affect photo download latency, are complementary to existing distributed erasure coding techniques, can efficiently be converted to the standard JPEG user devices expect, and significantly increase compression. We implement our photo recompression techniques in two novel codecs, ROMP and L-ROMP. ROMP is a lossless JPEG recompression codec that compresses typical photos 15% over standard JPEG. L-ROMP is a lossy JPEG recompression codec that distorts photos in a perceptually un-noticeable way and typically achieves 28% compression over standard JPEG. We estimate the benefits of our approach on Facebook's photo stack and find that our approaches can reduce the photo storage by 0.3-0.9× the logical size of the stored photos, and offer additional, collateral benefits to the photo caching stack, including 5-11% fewer requests to the backend storage, 15-31% reduction in wide-area bandwidth, and 16% reduction in external bandwidth.

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