3 research outputs found
Digital Video Inpainting Detection Using Correlation Of Hessian Matrix
The use of digital video during forensic investigation helps in providing evidence related to crime scene. However, due to freely available user friendly video editing tools, the forgery of acquired digital videos that are used as evidence in a law suit is now simpler and faster. As a result, it has become easier for manipulators to alter the contents of digital evidence. For instance, inpainting technique is used to remove an object from a video without leaving any artefact of illegal tampering. Therefore, this paper presents a technique for detecting and locating inpainting forgery in a video sequence with static camera motion. Our technique exploits statistical correlation of Hessian matrix (SCHM) to detect and locate tampered regions within a video sequence. The results of our experiments prove that the technique effectively detect and locate areas which are tampered using both texture and structure based inpainting with an average precision rate of 99.79% and an average false positive rate of 0.29%
Flexible encoder and decoder of low density parity check codes
Π£ Π΄ΠΈΡΠ΅ΡΡΠ°ΡΠΈΡΠΈ ΡΡ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π° Π±ΡΠ·Π°, ΡΠ»Π΅ΠΊΡΠΈΠ±ΠΈΠ»Π½Π° ΠΈ Ρ
Π°ΡΠ΄Π²Π΅ΡΡΠΊΠΈ Π΅ΡΠΈΠΊΠ°ΡΠ½Π° ΡΠ΅ΡΠ΅ΡΠ° Π·Π°
ΠΊΠΎΠ΄ΠΎΠ²Π°ΡΠ΅ ΠΈ Π΄Π΅ΠΊΠΎΠ΄ΠΎΠ²Π°ΡΠ΅ ΠΈΠ·ΡΠ·Π΅ΡΠ½ΠΎ Π½Π΅ΡΠ΅Π³ΡΠ»Π°ΡΠ½ΠΈΡ
ΠΊΠΎΠ΄ΠΎΠ²Π° ΡΠ° ΠΏΡΠΎΠ²Π΅ΡΠ°ΠΌΠ° ΠΏΠ°ΡΠ½ΠΎΡΡΠΈ ΠΌΠ°Π»Π΅ Π³ΡΡΡΠΈΠ½Π΅
(Π΅Π½Π³Π». low-density parity-check, LDPC, codes) Π·Π°Ρ
ΡΠ΅Π²Π°Π½Π° Ρ ΡΠ°Π²ΡΠ΅ΠΌΠ΅Π½ΠΈΠΌ ΠΊΠΎΠΌΡΠ½ΠΈΠΊΠ°ΡΠΈΠΎΠ½ΠΈΠΌ
ΡΡΠ°Π½Π΄Π°ΡΠ΄ΠΈΠΌΠ°.
ΠΠ΅Π΄Π°Π½ Π΄Π΅ΠΎ Π΄ΠΎΠΏΡΠΈΠ½ΠΎΡΠ° Π΄ΠΈΡΠ΅ΡΡΠ°ΡΠΈΡΠ΅ ΡΠ΅ Ρ Π½ΠΎΠ²ΠΎΡ Π΄Π΅Π»ΠΈΠΌΠΈΡΠ½ΠΎ ΠΏΠ°ΡΠ°Π»Π΅Π»Π½ΠΎΡ Π°ΡΡ
ΠΈΡΠ΅ΠΊΡΡΡΠΈ LDPC
ΠΊΠΎΠ΄Π΅ΡΠ° Π·Π° ΠΏΠ΅ΡΡ Π³Π΅Π½Π΅ΡΠ°ΡΠΈΡΡ ΠΌΠΎΠ±ΠΈΠ»Π½ΠΈΡ
ΠΊΠΎΠΌΡΠ½ΠΈΠΊΠ°ΡΠΈΡΠ°. ΠΡΡ
ΠΈΡΠ΅ΠΊΡΡΡΠ° ΡΠ΅ Π·Π°ΡΠ½ΠΎΠ²Π°Π½Π° Π½Π°
ΡΠ»Π΅ΠΊΡΠΈΠ±ΠΈΠ»Π½ΠΎΡ ΠΌΡΠ΅ΠΆΠΈ Π·Π° ΠΊΡΡΠΆΠ½ΠΈ ΠΏΠΎΠΌΠ΅ΡΠ°Ρ ΠΊΠΎΡΠ° ΠΎΠΌΠΎΠ³ΡΡΠ°Π²Π° ΠΏΠ°ΡΠ°Π»Π΅Π»Π½ΠΎ ΠΏΡΠΎΡΠ΅ΡΠΈΡΠ°ΡΠ΅ Π²ΠΈΡΠ΅ Π΄Π΅Π»ΠΎΠ²Π°
ΠΊΠΎΠ½ΡΡΠΎΠ»Π½Π΅ ΠΌΠ°ΡΡΠΈΡΠ΅ ΠΊΡΠ°ΡΠΊΠΈΡ
ΠΊΠΎΠ΄ΠΎΠ²Π° ΡΠΈΠΌΠ΅ ΡΠ΅ ΠΎΡΡΠ²Π°ΡΡΡΠ΅ ΡΠ»ΠΈΡΠ°Π½ Π½ΠΈΠ²ΠΎ ΠΏΠ°ΡΠ°Π»Π΅Π»ΠΈΠ·ΠΌΠ° ΠΊΠ°ΠΎ ΠΈ ΠΏΡΠΈ
ΠΊΠΎΠ΄ΠΎΠ²Π°ΡΡ Π΄ΡΠ³Π°ΡΠΊΠΈΡ
ΠΊΠΎΠ΄ΠΎΠ²Π°. ΠΠΎΡΠ΅Π΄ Π°ΡΡ
ΠΈΡΠ΅ΠΊΡΡΡΠ°Π»Π½ΠΎΠ³ ΡΠ΅ΡΠ΅ΡΠ°, ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π° ΡΠ΅ ΠΎΠΏΡΠΈΠΌΠΈΠ·Π°ΡΠΈΡΠ°
ΡΠ΅Π΄ΠΎΡΠ»Π΅Π΄Π° ΠΏΡΠΎΡΠ΅ΡΠΈΡΠ°ΡΠ° ΠΊΠΎΠ½ΡΡΠΎΠ»Π½Π΅ ΠΌΠ°ΡΡΠΈΡΠ΅ Π·Π°ΡΠ½ΠΎΠ²Π°Π½Π° Π½Π° Π³Π΅Π½Π΅ΡΠΈΡΠΊΠΎΠΌ Π°Π»Π³ΠΎΡΠΈΡΠΌΡ, ΠΊΠΎΡΠ°
ΠΎΠΌΠΎΠ³ΡΡΠ°Π²Π° ΠΏΠΎΡΡΠΈΠ·Π°ΡΠ΅ Π²Π΅Π»ΠΈΠΊΠΈΡ
ΠΏΡΠΎΡΠΎΠΊΠ°, ΠΌΠ°Π»ΠΎΠ³ ΠΊΠ°ΡΡΠ΅ΡΠ° ΠΈ ΡΡΠ΅Π½ΡΡΠ½ΠΎ Π½Π°ΡΠ±ΠΎΡΠ΅ Π΅ΡΠΈΠΊΠ°ΡΠ½ΠΎΡΡΠΈ
ΠΈΡΠΊΠΎΡΠΈΡΡΠ΅ΡΠ° Ρ
Π°ΡΠ΄Π²Π΅ΡΡΠΊΠΈΡ
ΡΠ΅ΡΡΡΡΠ°.
Π£ Π΄ΡΡΠ³ΠΎΠΌ Π΄Π΅Π»Ρ Π΄ΠΈΡΠ΅ΡΡΠ°ΡΠΈΡΠ΅ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΎ ΡΠ΅ Π½ΠΎΠ²ΠΎ Π°Π»Π³ΠΎΡΠΈΡΠ°ΠΌΡΠΊΠΎ ΠΈ Π°ΡΡ
ΠΈΡΠ΅ΠΊΡΡΡΠ°Π»Π½ΠΎ ΡΠ΅ΡΠ΅ΡΠ΅
Π·Π° Π΄Π΅ΠΊΠΎΠ΄ΠΎΠ²Π°ΡΠ΅ ΡΡΡΡΠΊΡΡΡΠΈΡΠ°Π½ΠΈΡ
LDPC ΠΊΠΎΠ΄ΠΎΠ²Π°. Π§Π΅ΡΡΠΎ ΠΊΠΎΡΠΈΡΡΠ΅Π½ΠΈ ΠΏΡΠΈΡΡΡΠΏ Ρ LDPC Π΄Π΅ΠΊΠΎΠ΄Π΅ΡΠΈΠΌΠ°
ΡΠ΅ ΡΠ»ΠΎΡΠ΅Π²ΠΈΡΠΎ Π΄Π΅ΠΊΠΎΠ΄ΠΎΠ²Π°ΡΠ΅, ΠΊΠΎΠ΄ ΠΊΠΎΠ³Π° ΡΠ΅ ΡΡΠ»Π΅Π΄ ΠΏΡΠΎΡΠΎΡΠ½Π΅ ΠΎΠ±ΡΠ°Π΄Π΅ ΡΠ°Π²ΡΠ°ΡΡ Ρ
Π°Π·Π°ΡΠ΄ΠΈ ΠΏΠΎΠ΄Π°ΡΠ°ΠΊΠ° ΠΊΠΎΡΠΈ
ΡΠΌΠ°ΡΡΡΡ ΠΏΡΠΎΡΠΎΠΊ. ΠΠ΅ΠΊΠΎΠ΄Π΅Ρ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ Ρ Π΄ΠΈΡΠ΅ΡΡΠ°ΡΠΈΡΠΈ Ρ ΠΊΠΎΠ½ΡΠ»ΠΈΠΊΡΠ½ΠΈΠΌ ΡΠΈΡΡΠ°ΡΠΈΡΠ°ΠΌΠ° Π½Π° ΠΏΠΎΠ³ΠΎΠ΄Π°Π½
Π½Π°ΡΠΈΠ½ ΠΊΠΎΠΌΠ±ΠΈΠ½ΡΡΠ΅ ΡΠ»ΠΎΡΠ΅Π²ΠΈΡΠΎ ΠΈ ΡΠΈΠΌΡΠ»ΡΠ°Π½ΠΎ Π΄Π΅ΠΊΠΎΠ΄ΠΎΠ²Π°ΡΠ΅ ΡΠΈΠΌΠ΅ ΡΠ΅ ΠΈΠ·Π±Π΅Π³Π°Π²Π°ΡΡ ΡΠΈΠΊΠ»ΡΡΠΈ ΠΏΠ°ΡΠ·Π΅
ΠΈΠ·Π°Π·Π²Π°Π½ΠΈ Ρ
Π°Π·Π°ΡΠ΄ΠΈΠΌΠ° ΠΏΠΎΠ΄Π°ΡΠ°ΠΊΠ°. ΠΠ²Π°Ρ ΠΏΡΠΈΡΡΡΠΏ Π΄Π°ΡΠ΅ ΠΌΠΎΠ³ΡΡΠ½ΠΎΡΡ Π·Π° ΡΠ²ΠΎΡΠ΅ΡΠ΅ Π²Π΅Π»ΠΈΠΊΠΎΠ³ Π±ΡΠΎΡΠ° ΡΡΠ΅ΠΏΠ΅Π½ΠΈ
ΠΏΡΠΎΡΠΎΡΠ½Π΅ ΠΎΠ±ΡΠ°Π΄Π΅ ΡΠΈΠΌΠ΅ ΡΠ΅ ΠΏΠΎΡΡΠΈΠΆΠ΅ Π²ΠΈΡΠΎΠΊΠ° ΡΡΠ΅ΡΡΠ°Π½ΠΎΡΡ ΡΠΈΠ³Π½Π°Π»Π° ΡΠ°ΠΊΡΠ°. ΠΠΎΠ΄Π°ΡΠ½ΠΎ, ΡΠ΅Π΄ΠΎΡΠ»Π΅Π΄
ΠΏΡΠΎΡΠ΅ΡΠΈΡΠ°ΡΠ° ΠΊΠΎΠ½ΡΡΠΎΠ»Π½Π΅ ΠΌΠ°ΡΡΠΈΡΠ΅ ΡΠ΅ ΠΎΠΏΡΠΈΠΌΠΈΠ·ΠΎΠ²Π°Π½ ΠΊΠΎΡΠΈΡΡΠ΅ΡΠ΅ΠΌ Π³Π΅Π½Π΅ΡΠΈΡΠΊΠΎΠ³ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° Π·Π°
ΠΏΠΎΠ±ΠΎΡΡΠ°Π½Π΅ ΠΏΠ΅ΡΡΠΎΡΠΌΠ°Π½ΡΠ΅ ΠΊΠΎΠ½ΡΡΠΎΠ»Π΅ Π³ΡΠ΅ΡΠ°ΠΊΠ°. ΠΡΡΠ²Π°ΡΠ΅Π½ΠΈ ΡΠ΅Π·ΡΠ»ΡΠ°ΡΠΈ ΠΏΠΎΠΊΠ°Π·ΡΡΡ Π΄Π°, Ρ ΠΏΠΎΡΠ΅ΡΠ΅ΡΡ ΡΠ°
ΡΠ΅ΡΠ΅ΡΠ΅Π½ΡΠ½ΠΈΠΌ ΡΠ΅ΡΠ΅ΡΠΈΠΌΠ°, ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΈ Π΄Π΅ΠΊΠΎΠ΄Π΅Ρ ΠΎΡΡΠ²Π°ΡΡΡΠ΅ Π·Π½Π°ΡΠ°ΡΠ½Π° ΠΏΠΎΠ±ΠΎΡΡΠ°ΡΠ° Ρ ΠΏΡΠΎΡΠΎΠΊΡ ΠΈ
Π½Π°ΡΠ±ΠΎΡΡ Π΅ΡΠΈΠΊΠ°ΡΠ½ΠΎΡΡ Π·Π° ΠΈΡΡΠ΅ ΠΏΠ΅ΡΡΠΎΡΠΌΠ°Π½ΡΠ΅ ΠΊΠΎΠ½ΡΡΠΎΠ»Π΅ Π³ΡΠ΅ΡΠ°ΠΊΠ°.The dissertation proposes high speed, flexible and hardware efficient solutions for coding and
decoding of highly irregular low-density parity-check (LDPC) codes, required by many modern
communication standards.
The first part of the dissertationβs contributions is in the novel partially parallel LDPC
encoder architecture for 5G. The architecture was built around the flexible shifting network that
enables parallel processing of multiple parity check matrix elements for short to medium code
lengths, thus providing almost the same level of parallelism as for long code encoding. In addition,
the processing schedule was optimized for minimal encoding time using the genetic algorithm. The
optimization procedure contributes to achieving high throughputs, low latency, and up to date the
best hardware usage efficiency (HUE).
The second part proposes a new algorithmic and architectural solution for structured LDPC
code decoding. A widely used approach in LDPC decoders is a layered decoding schedule, which
frequently suffers from pipeline data hazards that reduce the throughput. The decoder proposed in
the dissertation conveniently incorporates both the layered and the flooding schedules in cases when
hazards occur and thus facilitates LDPC decoding without stall cycles caused by pipeline hazards.
Therefore, the proposed architecture enables insertion of many pipeline stages, which consequently
provides a high operating clock frequency. Additionally, the decoding schedule was optimized for
better signal-to-noise ratio (SNR) performance using genetic algorithm. The obtained results show
that the proposed decoder achieves great throughput increase and the best HUE when compared
with the state of the art for the same SNR performance