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ARXIV:2605.05148 · IMAGE COMPRESSION · SUBMITTED 07 MAY · 20:25 UTC · FRESHNESS STALE
ARXIV:2605.05148IMAGE COMPRESSIONSUBMITTED 07 MAY · 20:25 UTCFRESHNESS STALEKedar Tatwawadi · Parisa Rahimzadeh · Zhanghao Sun · Zhiqi Chen · Ziyun Yang · Sanjay Nair · +2 at arXiv
A new learned image codec significantly improves the tradeoff between speed and perceptual quality, offering substantial bitrate savings compared to existing standards and other learned codecs.
Opportunity summary
Pain A new learned image codec significantly improves the tradeoff between speed and perceptual quality, offering substantial bitrate savings compared to existing standards and other learned codecs.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A new learned image codec significantly improves the tradeoff between speed and perceptual quality, offering substantial bitrate savings compared to existing standards and other learned codecs. Despite this potential, a perceptual yet practical image…
One of the major differentiators unlocked by learned codecs relative to their hard-coded traditional counterparts is their ability to be optimized directly to appeal to the human visual system. Despite this potential, a perceptual…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. We then perform performance-aware neural architecture search over millions of backbone configurations to identify models that achieve the target on-device runtime while maximizing compression…
Image Compression moved forward this cycle; last verified May 2026. Public score 7.0/10.
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Score7.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
A new learned image codec significantly improves the tradeoff between speed and perceptual quality, offering substantial bitrate savings compared to existing standards and other learned codecs.
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Paper Pack
10.48550/arXiv.2605.05148A new learned image codec significantly improves the tradeoff between speed and perceptual quality, offering substantial bitrate savings compared to existing standards and other learned codecs.
Abstract
One of the major differentiators unlocked by learned codecs relative to their hard-coded traditional counterparts is their ability to be optimized directly to appeal to the human visual system. Despite this potential, a perceptual yet practical image codec is yet to be proposed. In this work, we aim to close this gap. We conduct a comprehensive study of the key modeling choices that govern the design of a practical learned image codec, jointly optimized for perceptual quality and runtime -- including within the ablations several novel techniques. We then perform performance-aware neural architecture search over millions of backbone configurations to identify models that achieve the target on-device runtime while maximizing compression performance as captured by perceptual metrics. We combine the various optimizations to construct a new codec that achieves a significantly improved tradeoff between speed and perceptual quality. Based on rigorous subjective user studies, it provides 2.3-3x bitrate savings against AV1, AV2, VVC, ECM and JPEG-AI, and 20-40% bitrate savings against the best learned codec alternatives. At the same time, on an iPhone 17 Pro Max, it encodes 12MP images as fast as 230ms, and decodes them in 150ms -- faster than most top ML-based codecs run on a V100 GPU.
Source availability
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Extraction status
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Proof status
unverified0 refs; 3 sources; 50% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
Export
Preparing verified analysis
Dimensions overall score 7.0
PROBLEM
A new learned image codec significantly improves the tradeoff between speed and perceptual quality, offering substantial bitrate savings compared to existing standards and other learned codecs. Despite this potential, a perceptual yet practical image codec is yet to be proposed.
METHOD
One of the major differentiators unlocked by learned codecs relative to their hard-coded traditional counterparts is their ability to be optimized directly to appeal to the human visual system. Despite this potential, a perceptual yet practical image codec is yet to be proposed.
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. We then perform performance-aware neural architecture search over millions of backbone configurations to identify models that achieve the target on-device runtime while maximizing compression performance...
WHY NOW
Image Compression moved forward this cycle; last verified May 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A new learned image codec significantly improves the tradeoff between speed and perceptual quality, offering substantial bitrate savings compared to existing standards and other learned codecs. Despite this potential, a perceptual yet practical image codec is yet to be proposed.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
One of the major differentiators unlocked by learned codecs relative to their hard-coded traditional counterparts is their ability to be optimized directly to appeal to the human visual system. Despite this potential, a perceptual yet practical image codec is yet to be proposed.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. We then perform performance-aware neural architecture search over millions of backbone configurations to identify models that achieve the target on-device runtime while maximizing compression performance as captured by perceptual metrics.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Image Compression moved forward this cycle; last verified May 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Concepts
Methods
Materials
Markets
Competitors
A new learned image codec significantly improves the tradeoff between speed and perceptual quality, offering substantial bitrate savings compared to existing standards and other learned codecs.
Segment
Image Compression
Adoption evidence
No public code link in the paper record yet
Commercial read
7.0/10 public viability
Direct
Adjacent
Substitute
Unknown
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CITED BY
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Foundation
Commercially relevant
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2/3 checks · 67%
Build Passport
Build passport pending - Proof Lab budget No verified cost estimate / $7.00 cap
status
missing
reason
passport_row_missing
proof status
unverified
cost/budget
No verified cost estimate
confidence low
next verification path
Build brief missing until Build Passport data exists.
Source missing: Build Passport payload.
Experiment plan missing until prototype path is available.
No prototype path attached.
Validation checklist missing until required assets, cost, and regulatory flags are verified.
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Evidence coverage
OpportunityKernel evidence_receipt
0 refs / 3 sources / 50% coverage
stale
Verify missing sources before using this as buyer proof. verified:false
Build readiness
BuildPassport EvidenceState
passport absent
stale
Run Proof Lab or inspect typed missing state. verified:false
Artifact maturity
GitHub and Hugging Face maturity payloads
No public artifact surface observed
stale
Open source artifacts or mark the gap as missing. verified:false
Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
No Build Passport payload attached.
Gaps
Next test
Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
Current read
Buyer urgency is not verified from source.
Evidence
0 references, 3 sources, 50% evidence coverage.
Gaps
Next test
Collect buyer interview, deployment evidence, or cited demand signal.
Buyer clarity
missing
Current read
No budget owner is verified for this paper.
Evidence
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Gaps
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Map target operator, economic buyer, and procurement trigger.
Defensibility
missing
Current read
Defensibility signals are missing.
Evidence
No defensibility receipt attached.
Gaps
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Refresh defensibility bars with source receipts.
Integration burden
missing
Current read
No public implementation surface observed.
Evidence
No GitHub or Hugging Face payload attached.
Gaps
Next test
Write integration checklist from prototype path and target workflow.
Capital intensity
missing
Current read
No observed cost estimate is verified.
Evidence
Cost passport has no observed_usd value.
Gaps
Next test
Run cost passport or mark the cost field not applicable.
Regulatory load
missing
Current read
No regulatory classification is attached.
Evidence
Build Passport ledger does not include regulatory flags.
Gaps
Next test
Classify regulatory flags before commercialization planning.
No named scientific founder assigned.
Paper authors are not treated as operators without consent.
People
No named person assigned.
Gaps
Next verification path
Prototype owner missing.
Build Passport does not name an implementer.
People
No named person assigned.
Gaps
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Operator workflow not sourced.
No buyer or workflow interview attached.
People
No named person assigned.
Gaps
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People
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Gaps
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Regulatory need unclassified.
No clinical or regulatory source attached.
People
No named person assigned.
Gaps
Next verification path
ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
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FORESIGHT
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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COMPETITIVE LANDSCAPE UPDATES
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RELATED PAPER UPDATES
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SIGNAL CANVAS HISTORY AND DELTAS
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TIMELINE
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BUZZ
Buzz trend pending.