ADV611JST Analog Devices Inc, ADV611JST Datasheet - Page 4
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ADV611JST
Manufacturer Part Number
ADV611JST
Description
IC CODEC DIGITAL CCTV 120LQFP
Manufacturer
Analog Devices Inc
Type
Video Codecr
Datasheet
1.ADV611JSTZ.pdf
(46 pages)
Specifications of ADV611JST
Rohs Status
RoHS non-compliant
Data Interface
Serial
Resolution (bits)
8 b
Sigma Delta
No
Voltage - Supply, Digital
4.5 V ~ 5.5 V
Operating Temperature
0°C ~ 70°C
Mounting Type
Surface Mount
Package / Case
120-LQFP
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ADV611/ADV612
INTERNAL ARCHITECTURE
The ADV611/ADV612 is composed of eight blocks. Three of
these blocks are interface blocks and five are processing blocks.
The interface blocks are the Digital Video I/O Port, the Host
I/O Port and the external DRAM manager. The processing
blocks are the Wavelet Kernel, the On-Chip Transform Buffer,
the Programmable Quantizer, the Run Length Coder and the
Huffman Coder.
Digital Video I/O Port
Provides a real-time uncompressed video interface to support a
broad range of component digital video formats, including “D1.”
Host I/O Port and FIFO
Carries control, status, and compressed video to and from the
host processor. A 512 position by 32-bit FIFO buffers the com-
pressed video stream between the host and the Huffman Coder.
Hardware Field Rate Reduction
In CCTV applications it is often desirable to reduce the field
rate to achieve the highest possible compression. The ADV611/
ADV612 have special hardware to permit this function. It is
possible to set a register on the ADV611/ADV612 during en-
code mode that will automatically reduce the field rate. This is a
5-bit register that allows up to 31 fields to be “skipped.”
Stall Mode
It is possible to stall or halt the ADV611/ADV612 at any time
during Encode Mode. This allows the user to feed uncompressed
video data to these parts and to stop indefinitely between fields
or even between pixels. This feature is useful when compressing
video that is not coming into the ADV611/ADV612 at sustained
V
any time during encode. Stall mode is enabled on the next clock
cycle after the pin is asserted.
Field Size Reporting
The ADV611/ADV612 have a read-only register that allows the
user to read the field size of the most recently compressed field.
This feature is useful in the feedback loop of a precise bit rate
controller. The data is valid after LCODE (unless an entire
compressed field resides in the internal FIFO).
DRAM Manager
Performs all tasks related to writing, reading and refreshing the
external DRAM. The external host buffer DRAM is used for
reordering and buffering quantizer input and output values.
Wavelet Kernel (Filters, Decimator, and Interpolator)
Gathers statistics on a per-field basis and includes a block of
filters, interpolators and decimators. The kernel calculates for-
ward and backward bi-orthogonal, two-dimensional, separable
wavelet transforms on horizontal scanned video data. This block
uses the internal transform buffer when performing wavelet
transforms calculated on an entire image’s data and so elimi-
nates any need for extremely fast external memories in an
ADV611/ADV612-based design.
On-Chip Transform Buffer
Provides an internal set of SRAM for use by the wavelet trans-
form kernel. Its function is to provide enough delay line storage
to support calculation of separable two dimensional wavelet
transforms for horizontally scanned images.
CLK
rates. Stall Mode is enabled by asserting the Stall pin at
–4–
Programmable Quantizer
Quantizes wavelet coefficients. Quantize controls are calculated
by the external DSP or host processor during encode operations
and de-quantize controls are extracted from the compressed
bitstream during decode. Each quantizer Bin Width is com-
puted by the BW calculator software to maintain a constant
compressed bit rate or constant quality bit rate. A Bin Width is
a per-block parameter the quantizer uses when determining the
number of bits to allocate to each block (subband).
Quality Box
The quality box is defined using the Video Area Registers that
are described in the Registers Descriptions section. The back-
ground contrast is controlled using Background Contrast Regis-
ters that are defined later in this document. It is possible to
control both parameters on a per-field basis during Encode
Mode. This enables the quality box to either move slowly across
the image or to instantaneously jump from one location to the
next.
Run Length Coder
Performs run length coding on zero data and models nonzero
data, encoding or decoding for more efficient Huffman coding.
This data coding is optimized across the subbands and varies
depending on the block being coded.
Huffman Coder
Performs Huffman coder and decoder functions on quantized
run-length coded coefficient values. The Huffman coder/de-
coder uses three ROM-coded Huffman tables that provide ex-
cellent performance for wavelet transformed video.
Field Truncation
It is possible to set a hard upper limit to the field size of each
field during Encode Mode. The Huffman Coder is able to de-
tect if the field size exceeds a preset threshold and then causes
the remaining Mallat block data to be zeroed out, therefore,
truncating the field’s data. The bitstream is truncated in such a
way that all end-of-field markers are inserted. This means that
the compressed bitstream can still be decompressed by any
hardware or software ADV6xx decoder. The only penalty is the
loss of Mallat blocks which, depending on how many are lost,
will degrade the image quality of the truncated field.
GENERAL THEORY OF OPERATION
The ADV611/ADV612 processor’s compression algorithm is
based on the bi-orthogonal (7, 9) wavelet transform, and imple-
ments field independent subband coding. Subband coders trans-
form two-dimensional spatial video data into spatial frequency
filtered subbands. The quantization and entropy encoding pro-
cesses provide the ADV611/ADV612’s data compression.
The wavelet theory, on which the ADV611/ADV612 is based, is
a new mathematical apparatus first explicitly introduced by
Morlet and Grossman in their works on geophysics during the
mid 80s. This theory became very popular in theoretical physics
and applied math. The late 80s and 90s have seen a dramatic
growth in wavelet applications such as signal and image process-
ing. For more on wavelet theory by Morlet and Grossman, see
Decomposition of Hardy Functions into Square Integrable Wavelets
of Constant Shape (journal citation listed in References section).
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